KR20230093242A - Syndrome comparison-based QBER estimation method and apparatus in QKD system - Google Patents

Abstract

In the present specification, in a method for estimating an error rate performed by a device in a quantum cryptography communication system, a random access (RA) preamble is received from another device, and a RAR is transmitted to the other device in response to the RA preamble. (random access response), performs a radio resource control (RRC) connection procedure with the other device, receives data from the other device, and decrypts the data based on key information, but the key information is determined based on the estimation of the error rate for selection key information received from another device, wherein the device receives first syndrome information from the other device, and based on the selection key information and a parity check matrix, second Syndrome information is derived, odd-numbered errors and even-numbered errors related to the selection key information are detected based on the first syndrome information and the second syndrome information, and based on the odd-numbered errors and the even-numbered errors Based on performing the estimation of the error rate, a method and apparatus characterized in that for estimating the error rate is provided.

Description

Syndrome comparison-based QBER estimation method and apparatus in QKD system

This specification relates to quantum communication systems.

Due to the advent of quantum computers, it has become possible to hack existing cryptographic systems based on mathematical complexity (eg, RSA, AES, etc.). To prevent hacking, quantum cryptographic communication is proposed.

Meanwhile, the present specification intends to provide a method for improving QBER estimation accuracy without key rate loss of quantum key information in a QBER estimation process among quantum secure communication techniques and an apparatus using the same.

According to an embodiment of the present specification, first syndrome information is received from the other device, second syndrome information is derived based on the selection key information and the parity check matrix, and the first syndrome information and the second syndrome information are derived. detecting odd-numbered errors and even-numbered errors related to the sorting key information based on information, and estimating the error rate based on the odd-numbered errors and the even-numbered errors; to provide.

According to the present specification, the problem of a method of estimating the QBER of an existing quantum cryptographic communication system can be solved through syndrome comparison obtained from a quantum key sifted in a transceiver. In addition, in the present specification, there are effects in terms of QBER estimation accuracy/leaked information amount/sifted key rate/security (possibility of restoring key information from leaked information) compared to the prior art.

Effects obtainable through specific examples of the present specification are not limited to the effects listed above. For example, there may be various technical effects that a person having ordinary skill in the related art can understand or derive from this specification. Accordingly, the specific effects of the present specification are not limited to those explicitly described in the present specification, and may include various effects that can be understood or derived from the technical features of the present specification.

1 illustrates a system structure of a New Generation Radio Access Network (NG-RAN) to which NR is applied.
Figure 2 illustrates the functional split between NG-RAN and 5GC.
3 shows an example of a 5G usage scenario to which the technical features of the present specification can be applied.
4 is a diagram showing an example of a communication structure that can be provided in a 6G system.
5 schematically illustrates an example of a perceptron structure.
6 schematically illustrates an example of a multilayer perceptron structure.
7 schematically illustrates a deep neural network example.
8 schematically illustrates an example of a convolutional neural network.
9 schematically illustrates an example of a filter operation in a convolutional neural network.
10 schematically illustrates an example of a neural network structure in which a cyclic loop exists.
11 schematically illustrates an example of an operating structure of a recurrent neural network.
12 shows an example of an electromagnetic spectrum.
13 is a diagram showing an example of a THz communication application.
14 is a diagram showing an example of an electronic element-based THz wireless communication transceiver.
15 is a diagram illustrating an example of a method for generating a THz signal based on an optical device, and FIG. 16 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical device.
17 illustrates a structure of a photonic source-based transmitter, and FIG. 18 illustrates a structure of an optical modulator.
19 schematically illustrates an example of quantum cryptographic communication.
20 schematically illustrates an example of a random sampling technique.
21 schematically shows the overall process and key length change of the QKD system.
22 schematically illustrates an example of a parity comparison technique.
23 schematically shows QBER estimation accuracy according to random sampling (TM) and actual QBER of PCM.
24 is a flowchart of a method for estimating an error rate according to an embodiment of the present specification.
25 schematically illustrates an example of a device according to an embodiment of the present specification.
26 is a flowchart of a method for estimating an error rate according to another embodiment of the present specification.
27 schematically illustrates a configuration diagram of a group detection technique including odd and even number errors through syndrome comparison of a transceiver.
FIG. 28 schematically shows an example (m=3) of detecting a case where an odd number of group errors occur using a 1-bit syndrome.
29 shows an entire process of detecting a group including an odd number of errors, and a process of estimating whether or not an odd number of errors are included is as follows.
FIG. 30 schematically illustrates an example (m=3) of detecting a case in which an even number of group errors occur using a syndrome.
31 shows an entire process of detecting a group including an even number of errors, and a process of estimating whether an even number of errors is included is as follows.
32 schematically illustrates an example of a syndrome-based QBER estimation method in a QKD system.
33 to 35 schematically show QBER estimation accuracy comparisons in three group lengths.
36 schematically shows an example of QBER estimation accuracy comparison of each technique based on the same amount of leakage information.
37 schematically shows an example of QBER estimation accuracy comparison of PCM (left) and inventive technique (right) according to sifted key length change.
38 is a flowchart of a method for estimating an error rate, from a (Bob Side's) device perspective, according to an embodiment of the present specification.
39 is a flowchart of a method for estimating an error rate from a (Bob Side's) device perspective, according to another embodiment of the present specification.
40 is a block diagram of an example of an apparatus for estimating an error rate, from a (Bob Side's) apparatus perspective, according to an embodiment of the present specification.
41 is a flowchart of a method for estimating an error rate, from a device perspective (of Alice Said), according to an embodiment of the present disclosure.
42 is a block diagram of an example of an apparatus for estimating an error rate, from a (Alice Said's) apparatus perspective, according to an embodiment of the present disclosure.
43 illustrates the communication system 1 applied to this specification.
44 illustrates a wireless device applicable to this specification.
45 illustrates another example of a wireless device applicable to the present specification.
46 illustrates a signal processing circuit for a transmission signal.
47 shows another example of a wireless device applied to this specification.
48 illustrates a portable device applied to this specification.
49 illustrates a vehicle or an autonomous vehicle applied in this specification.
50 illustrates a vehicle applied to this specification.
51 illustrates an XR device applied herein.
52 illustrates a robot applied in this specification.
53 illustrates an AI device applied to this specification.

In this specification, "A or B" may mean "only A", "only B", or "both A and B". In other words, "A or B (A or B)" in the present specification may be interpreted as "A and/or B (A and/or B)". For example, “A, B or C” as used herein means “only A”, “only B”, “only C”, or “any and all combinations of A, B and C ( any combination of A, B and C)".

A slash (/) or a comma (comma) used in this specification may mean "and/or". For example, "A/B" can mean "A and/or B". Accordingly, "A/B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B or C".

In this specification, "at least one of A and B" may mean "only A", "only B", or "both A and B". Also, in this specification, the expression "at least one of A or B" or "at least one of A and/or B" means "at least one It can be interpreted the same as "A and B (at least one of A and B) of

In addition, in this specification, "at least one of A, B and C" means "only A", "only B", "only C", or "A, B and C" It may mean "any combination of A, B and C". Also, "at least one of A, B or C" or "at least one of A, B and/or C" means It can mean "at least one of A, B and C".

Also, parentheses used in this specification may mean “for example”. Specifically, when indicated as “control information (PDCCH)”, “PDCCH” may be suggested as an example of “control information”. In other words, "control information" in this specification is not limited to "PDCCH", and "PDDCH" may be suggested as an example of "control information". Also, even when displayed as “control information (ie, PDCCH)”, “PDCCH” may be suggested as an example of “control information”.

Technical features that are individually described in one drawing in this specification may be implemented individually or simultaneously.

Hereinafter, new radio access technology (new RAT, NR) will be described.

As more and more communication devices require greater communication capacity, a need for improved mobile broadband communication compared to conventional radio access technology (RAT) has emerged. In addition, massive machine type communications (MTC), which provides various services anytime and anywhere by connecting multiple devices and objects, is also one of the major issues to be considered in next-generation communication. In addition, communication system design considering reliability and latency-sensitive services/terminals is being discussed. The introduction of next-generation wireless access technologies considering such expanded mobile broadband communication, massive MTC, URLLC (Ultra-Reliable and Low Latency Communication), etc. is being discussed, and in this specification, for convenience, the corresponding technology is called new RAT or NR.

1 illustrates a system structure of a New Generation Radio Access Network (NG-RAN) to which NR is applied.

Referring to FIG. 1, an NG-RAN may include a gNB and/or an eNB that provides user plane and control plane protocol termination to a UE. 1 illustrates a case including only gNB. gNB and eNB are connected to each other through an Xn interface. The gNB and the eNB are connected to a 5G Core Network (5GC) through an NG interface. More specifically, an access and mobility management function (AMF) is connected through an NG-C interface, and a user plane function (UPF) is connected through an NG-U interface.

Figure 2 illustrates the functional split between NG-RAN and 5GC.

Referring to FIG. 2, the gNB is inter-cell radio resource management (Inter Cell RRM), radio bearer management (RB control), connection mobility control (Connection Mobility Control), radio admission control (Radio Admission Control), measurement setup and provision (Measurement configuration & provision) and dynamic resource allocation. AMF can provide functions such as NAS security and idle state mobility handling. UPF may provide functions such as mobility anchoring and PDU processing. Session Management Function (SMF) may provide functions such as terminal IP address allocation and PDU session control.

3 shows an example of a 5G usage scenario to which the technical features of the present specification can be applied. The 5G usage scenario shown in FIG. 3 is just an example, and the technical features of the present specification may also be applied to other 5G usage scenarios not shown in FIG. 3 .

Referring to FIG. 3, the three major requirements areas of 5G are (1) enhanced mobile broadband (eMBB) area, (2) massive machine type communication (mMTC) area, and ( 3) It includes the ultra-reliable and low latency communications (URLLC) area. Some use cases may require multiple areas for optimization, while other use cases may focus on just one key performance indicator (KPI). 5G supports these diverse use cases in a flexible and reliable way.

eMBB focuses on overall improvements in data rate, latency, user density, capacity and coverage of mobile broadband access. eMBB targets a throughput of around 10 Gbps. eMBB goes far beyond basic mobile Internet access, and covers rich interactive work, media and entertainment applications in the cloud or augmented reality. Data is one of the key drivers of 5G, and we may not see dedicated voice services for the first time in the 5G era. In 5G, voice is expected to be handled simply as an application using the data connection provided by the communication system. The main causes of the increased traffic volume are the increase in content size and the increase in the number of applications requiring high data rates. Streaming services (audio and video), interactive video and mobile internet connections will become more widely used as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are rapidly growing in mobile communication platforms, which can be applied to both work and entertainment. Cloud storage is a particular use case driving the growth of uplink data rates. 5G is also used for remote work in the cloud, requiring much lower end-to-end latency to maintain a good user experience when tactile interfaces are used. In entertainment, for example, cloud gaming and video streaming are other key factors driving the demand for mobile broadband capabilities. Entertainment is essential on smartphones and tablets everywhere, including in highly mobile environments such as trains, cars and planes. Another use case is augmented reality for entertainment and information retrieval. Here, augmented reality requires very low latency and instantaneous amount of data.

mMTC is designed to enable communication between high-volume, low-cost devices powered by batteries, and is intended to support applications such as smart metering, logistics, field and body sensors. mMTC targets 10 years of batteries and/or 1 million devices per square kilometer. mMTC enables seamless connectivity of embedded sensors in all fields and is one of the most anticipated 5G use cases. Potentially, IoT devices are predicted to reach 20.4 billion by 2020. Industrial IoT is one area where 5G is playing a key role enabling smart cities, asset tracking, smart utilities, agriculture and security infrastructure.

URLLC enables devices and machines to communicate with high reliability, very low latency and high availability, making it ideal for vehicular communications, industrial controls, factory automation, remote surgery, smart grid and public safety applications. URLLC targets latency on the order of 1 ms. URLLC includes new services that will transform industries through ultra-reliable/low-latency links, such as remote control of critical infrastructure and autonomous vehicles. This level of reliability and latency is essential for smart grid control, industrial automation, robotics, and drone control and coordination.

Next, a number of usage examples included in the triangle of FIG. 3 will be described in more detail.

5G can complement fiber-to-the-home (FTTH) and cable-based broadband (or DOCSIS) as a means of delivering streams rated at hundreds of megabits per second to gigabits per second. Such high speeds may be required to deliver TV at resolutions of 4K and beyond (6K, 8K and beyond) as well as virtual reality (VR) and augmented reality (AR). VR and AR applications include almost immersive sports events. Certain applications may require special network settings. For example, in the case of VR games, game companies may need to integrate their core servers with the network operator's edge network servers to minimize latency.

Automotive is expected to be an important new driver for 5G, with many use cases for mobile communications to vehicles. For example, entertainment for passengers requires both high capacity and high mobile broadband. The reason is that future users will continue to expect high-quality connections regardless of their location and speed. Another use case in the automotive sector is augmented reality dashboards. Drivers can identify objects in the dark above what they are viewing through the front window via an augmented reality contrast board. The augmented reality dashboard displays overlaid information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable communication between vehicles, exchange of information between vehicles and supporting infrastructure, and exchange of information between vehicles and other connected devices (eg devices carried by pedestrians). A safety system can help reduce the risk of an accident by guiding the driver through an alternate course of action to make driving safer. The next step will be remotely controlled or self-driving vehicles. This requires very reliable and very fast communication between different autonomous vehicles and/or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving activities, leaving drivers to focus only on traffic anomalies that the vehicle itself cannot identify. The technological requirements of autonomous vehicles require ultra-low latency and ultra-high reliability to increase traffic safety to levels that humans cannot achieve.

Smart cities and smart homes, referred to as smart societies, will be embedded with high-density wireless sensor networks. A distributed network of intelligent sensors will identify conditions for cost- and energy-efficient maintenance of a city or home. A similar setup can be done for each household. Temperature sensors, window and heating controllers, burglar alarms and appliances are all connected wirelessly. Many of these sensors typically require low data rates, low power and low cost. However, real-time HD video, for example, may be required in certain types of devices for surveillance.

The consumption and distribution of energy, including heat or gas, is highly decentralized, requiring automated control of distributed sensor networks. A smart grid interconnects these sensors using digital information and communication technologies to gather information and act on it. This information can include supplier and consumer behavior, enabling the smart grid to improve efficiency, reliability, affordability, sustainability of production and distribution of fuels such as electricity in an automated manner. The smart grid can also be viewed as another low-latency sensor network.

The health sector has many applications that can benefit from mobile communications. The communication system may support telemedicine, which provides clinical care at a remote location. This can help reduce barriers to distance and improve access to health services that are not consistently available in remote rural areas. It is also used to save lives in critical care and emergencies. Mobile communication-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring is expensive to install and maintain. Thus, the possibility of replacing cables with reconfigurable wireless links is an attractive opportunity for many industries. However, achieving this requires that wireless connections operate with comparable latency, reliability and capacity to cables, and that their management be simplified. Low latency and very low error probability are the new requirements that need to be connected with 5G.

Logistics and freight tracking is an important use case for mobile communications enabling the tracking of inventory and packages from anywhere using location-based information systems. Logistics and freight tracking use cases typically require low data rates, but may require wide range and reliable location information.

Hereinafter, examples of next-generation communication (eg, 6G) that can be applied to the embodiments of the present specification will be described.

<6G System General>

6G (radio communications) systems are characterized by (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be four aspects such as intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing an example of requirements for a 6G system.

Per device peak data rate 1 Tbps E2E latency 1ms Maximum spectral efficiency 100 bps/Hz Mobility support Up to 1000km/hr Satellite integration Fully AI Fully Autonomous vehicles Fully XR Fully Haptic Communication Fully

6G systems include Enhanced mobile broadband (eMBB), Ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, Tactile internet, High throughput, High network capacity, High energy efficiency, Low backhaul and It may have key factors such as access network congestion and enhanced data security. FIG. 4 is a diagram showing an example of a communication structure that can be provided in a 6G system. It is expected to have wireless communication connectivity. URLLC, a key feature of 5G, will become even more important in 6G communications by providing end-to-end latency of less than 1 ms. The 6G system will have much better volume spectral efficiency as opposed to the frequently used area spectral efficiency. 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices will not need to be charged separately in 6G systems. In 6G, new network features may be: - Satellites integrated network: 6G is expected to integrate with satellites to provide a global mobile population. Integration of terrestrial, satellite and public networks into one wireless communication system is critical for 6G.

- Connected intelligence: unlike previous generations of wireless communication systems, 6G is revolutionary and will update the wireless evolution from “connected things” to “connected intelligence.” each procedure of processing).

- Seamless integration wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

- Ubiquitous super 3D connectivity: Access to networks and core network capabilities of drones and very low Earth orbit satellites will make super 3D connectivity in 6G ubiquitous.

In the new network characteristics of 6G as above, some general requirements can be as follows.

- Small cell networks: The idea of small cell networks has been introduced to improve received signal quality resulting in improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are an essential feature of 5G and Beyond 5G (5GB) and beyond communication systems. Therefore, the 6G communication system also adopts the characteristics of the small cell network.

- Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important feature of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

- High-capacity backhaul: A backhaul connection is characterized by a high-capacity backhaul network to support high-capacity traffic. High-speed fiber and free space optical (FSO) systems may be possible solutions to this problem.

- Radar technology integrated with mobile technology: High-precision localization (or location-based service) through communication is one of the features of 6G wireless communication systems. Thus, radar systems will be integrated with 6G networks.

- Softwarization and virtualization: Softwarization and virtualization are two important features fundamental to the design process in 5GB networks to ensure flexibility, reconfigurability and programmability. In addition, billions of devices can be shared in a shared physical infrastructure.

<Core implementation technology of 6G system>

Artificial Intelligence

The most important and newly introduced technology for the 6G system is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, the 6G system will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI in communications can simplify and enhance real-time data transmission. AI can use a plethora of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.

Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communications. In addition, AI can be a rapid communication in BCI (Brain Computer Interface). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

Recently, there have been attempts to integrate AI with wireless communication systems, but these have been focused on the application layer and network layer, especially deep learning in the field of wireless resource management and allocation. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and may include allocations, etc.

Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a downlink (DL) physical layer. Machine learning can also be used for antenna selection, power control, symbol detection, and the like in a MIMO system.

However, the application of DNN for transmission in the physical layer may have the following problems.

AI algorithms based on deep learning require a lot of training data to optimize training parameters. However, due to limitations in acquiring data in a specific channel environment as training data, a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a radio channel.

In addition, current deep learning mainly targets real signals. However, the signals of the physical layer of wireless communication are complex signals. In order to match the characteristics of wireless communication signals, further research on a neural network for detecting complex domain signals is needed.

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do. Machine learning requires data and a running model. In machine learning, data learning methods can be largely divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.

Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.

The learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called

The neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann Machine (RNN). there is.

An artificial neural network is an example of connecting several perceptrons.

5 schematically illustrates an example of a perceptron structure.

Referring to FIG. 5, when the input vector x=(x1,x2,...,xd) is input, each component is multiplied by the weight (W1,W2,...,Wd), and after summing up the results, The entire process of applying the activation function σ(·) is called a perceptron. The huge artificial neural network structure may extend the simplified perceptron structure shown in FIG. 5 and apply input vectors to different multi-dimensional perceptrons. For convenience of description, an input value or an output value is referred to as a node.

Meanwhile, the perceptron structure shown in FIG. 5 can be described as being composed of a total of three layers based on input values and output values. An artificial neural network with H number of (d + 1) dimensional perceptrons between ^{the 1st} layer and ^{the 2nd} layer and K number of (H + 1) dimensional perceptrons between the 2nd layer and the ^3rd layer can be expressed as shown in FIG. there is.

6 schematically illustrates an example of a multilayer perceptron structure.

The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all the layers located between the input layer and the output layer are called hidden layers. In the example of FIG. 6 , three layers are disclosed, but when counting the number of layers of an actual artificial neural network, the number of layers is counted excluding the input layer, so a total of two layers can be considered. An artificial neural network is composed of two-dimensionally connected perceptrons of basic blocks.

The above-described input layer, hidden layer, and output layer can be jointly applied to various artificial neural network structures such as CNN and RNN, which will be described later, as well as multi-layer perceptrons. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. In addition, the artificial neural network used for deep learning is called a deep neural network (DNN).

7 schematically illustrates a deep neural network example.

The deep neural network shown in FIG. 7 is a multi-layer perceptron consisting of 8 hidden layers + 8 output layers. The multilayer perceptron structure is expressed as a fully-connected neural network. In a fully-connected neural network, there is no connection relationship between nodes located on the same layer, and a connection relationship exists only between nodes located on adjacent layers. DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to identify the correlation characteristics between inputs and outputs. Here, the correlation characteristic may mean a joint probability of input and output.

On the other hand, depending on how a plurality of perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

8 schematically illustrates an example of a convolutional neural network.

In DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, in FIG. 8, it can be assumed that the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically (convolutional neural network structure of FIG. 8). In this case, a weight is added for each connection in the connection process from one input node to the hidden layer, so a total of hХw weights must be considered. Since there are hХw nodes in the input layer, a total of h ² w ² weights are required between two adjacent layers.

The convolutional neural network of FIG. 8 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering all mode connections between adjacent layers, it is assumed that there is a filter with a small size, and FIG. 9 As shown in , weighted sum and activation function calculations are performed for overlapping filters.

9 schematically illustrates an example of a filter operation in a convolutional neural network.

One filter has weights corresponding to the number of filters, and learning of weights can be performed so that a specific feature on an image can be extracted as a factor and output. In FIG. 9 , a 3Х3 size filter is applied to the 3Х3 region at the top left of the input layer, and an output value obtained by performing a weighted sum and an activation function operation for a corresponding node is stored in z ₂₂ .

While scanning the input layer, the filter moves by a certain distance horizontally and vertically, performs weighted sum and activation function calculations, and places the output value at the position of the current filter. This operation method is similar to the convolution operation for images in the field of computer vision, so the deep neural network of this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. Also, a neural network having a plurality of convolutional layers is referred to as a deep convolutional neural network (DCNN).

In the convolution layer, the number of weights can be reduced by calculating a weighted sum by including only nodes located in a region covered by the filter from the node where the current filter is located. This allows one filter to be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which a physical distance in a 2D area is an important criterion. Meanwhile, in the CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through a convolution operation of each filter.

Meanwhile, there may be data whose sequence characteristics are important according to data attributes. Considering the length variability and precedence relationship of these sequence data, input each element on the data sequence one by one at each time step, and input the output vector (hidden vector) of the hidden layer output at a specific time point together with the next element on the sequence A structure in which this method is applied to an artificial neural network is called a recurrent neural network structure.

10 schematically illustrates an example of a neural network structure in which a cyclic loop exists.

Referring to FIG. 10, a recurrent neural network (RNN) assigns an element (x1(t), x2(t), ,..., xd(t)) of any line t on a data sequence to a fully connected neural network. In the process of inputting, the immediately preceding time point t-1 inputs the hidden vector (z1(t-1), z2(t-1),..., zH(t-1)) together to calculate the weighted sum and activation function structure that is applied. The reason why the hidden vector is transmitted to the next time point in this way is that information in the input vector at previous time points is regarded as being accumulated in the hidden vector of the current time point.

11 schematically illustrates an example of an operating structure of a recurrent neural network.

Referring to FIG. 11, the recurrent neural network operates in a sequence of predetermined views with respect to an input data sequence.

When the input vector (x1(t), x2(t), ,..., xd(t)) at time 　1 is input to the recurrent neural network, the hidden vector 　(z1(1),z2(1),.. .,zH(1)) is input together with the input vector of time 　2 (x1(2),x2(2),...,xd(2)), and the vector of the hidden layer 　 (z1( 2),z2(2) ,...,zH(2)). This process is repeatedly performed until time point 2, point 3, ,,, point T.

Meanwhile, when a plurality of hidden layers are disposed within a recurrent neural network, this is referred to as a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be usefully applied to sequence data (eg, natural language processing).

As a neural network core used as a learning method, in addition to DNN, CNN, and RNN, Restricted Boltzmann Machine (RBM), deep belief networks (DBN), and Deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Recently, there have been attempts to integrate AI with wireless communication systems, but these have been focused on the application layer and network layer, especially deep learning in the field of wireless resource management and allocation. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and may include allocations, etc.

Terahertz (THz) communication

The data rate can be increased by increasing the bandwidth. This can be done using sub-THz communication with wide bandwidth and applying advanced massive MIMO technology. THz waves, also known as submillimeter radiation, typically represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm. The 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications. 6G cellular communication capacity increases when added to the sub-THz band mmWave band. Of the defined THz bands, 300 GHz-3 THz is in the far-infrared (IR) frequency band. The 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.

12 shows an example of an electromagnetic spectrum.

The main characteristics of THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are indispensable). The narrow beamwidth produced by the highly directional antenna reduces interference. The small wavelength of the THz signal allows a much larger number of antenna elements to be incorporated into devices and BSs operating in this band. This enables advanced adaptive array technology to overcome range limitations.

Optical wireless technology

OWC technology is intended for 6G communications in addition to RF-based communications for all possible device-to-access networks. These networks access network-to-backhaul/fronthaul network connections. OWC technology is already in use after the 4G communication system, but will be more widely used to meet the needs of the 6G communication system. OWC technologies such as light fidelity, visible light communication, optical camera communication, and FSO communication based on a wide band are already well-known technologies. Communications based on optical wireless technology can provide very high data rates, low latency and secure communications. LiDAR can also be used for ultra-high resolution 3D mapping in 6G communication based on broadband.

FSO backhaul network

The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network. Thus, data transmission in FSO systems is similar to fiber optic systems. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks. With FSO, very long-distance communication is possible even at a distance of 10,000 km or more. FSO supports high-capacity backhaul connectivity for remote and non-remote locations such as ocean, space, underwater and isolated islands. FSO also supports cellular BS connections.

Massive MIMO technology

One of the key technologies to improve spectral efficiency is to apply MIMO technology. As MIMO technology improves, so does the spectral efficiency. Therefore, massive MIMO technology will be important in 6G systems. Since MIMO technology uses multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must be considered as important so that data signals can be transmitted through more than one path.

block chain

Blockchain will be an important technology for managing large amounts of data in future communication systems. Blockchain is a form of distributed ledger technology, where a distributed ledger is a database that is distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchain is managed as a peer-to-peer network. It can exist without being managed by a centralized authority or server. Data on a blockchain is collected together and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain is the perfect complement to the IoT at scale with inherently improved interoperability, security, privacy, reliability and scalability. Thus, blockchain technology provides multiple capabilities such as interoperability between devices, traceability of large amounts of data, autonomous interaction of other IoT systems, and large-scale connection reliability in 6G communication systems.

3D Networking

The 6G system integrates terrestrial and air networks to support vertical expansion of user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in terms of height and related degrees of freedom makes 3D connections quite different from traditional 2D networks.

quantum communication

In the context of 6G networks, unsupervised reinforcement learning of networks is promising. Supervised learning approaches cannot label the vast amount of data generated by 6G. Labeling is not required in unsupervised learning. Thus, this technique can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows networks to operate in a truly autonomous way.

drone

Unmanned Aerial Vehicles (UAVs) or drones will be an important element in 6G wireless communications. In most cases, high-speed data wireless connectivity is provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs have certain features not found in fixed BS infrastructures, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility. During emergencies, such as natural disasters, the deployment of terrestrial communications infrastructure is not economically feasible and cannot provide services in sometimes volatile environments. UAVs can easily handle this situation. UAV will become a new paradigm in the field of wireless communication. This technology facilitates three basic requirements of a wireless network: eMBB, URLLC and mMTC. UAVs can also support multiple purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, accident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.

Cell-free Communication

The tight integration of multiple frequencies and heterogeneous communication technologies is critical for 6G systems. As a result, users can seamlessly move from one network to another without having to make any manual configuration on the device. The best network is automatically selected from available communication technologies. This will break the limitations of the cell concept in wireless communication. Currently, user movement from one cell to another causes too many handovers in high-density networks, resulting in handover failures, handover delays, data loss and ping-pong effects. 6G cell-free communication will overcome all of this and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies and different heterogeneous radios of devices.

Integration of wireless information and energy transmission

WIET uses the same fields and waves as wireless communication systems. In particular, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the lifetime of battery charging wireless systems. Thus, battery-less devices will be supported in 6G communications.

Integration of sensing and communication

Autonomous radio networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

Integration of access backhaul networks

In 6G, the density of access networks will be enormous. Each access network is connected by fiber and backhaul connections such as FSO networks. To cope with the very large number of access networks, there will be tight integration between access and backhaul networks.

Holographic Beamforming

Beamforming is a signal processing procedure that adjusts an antenna array to transmit radio signals in a specific direction. A subset of smart antennas or advanced antenna systems. Beamforming technology has several advantages such as high call-to-noise ratio, interference avoidance and rejection, and high network efficiency. Hologram beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

big data analytics

Big data analysis is a complex process for analyzing various large data sets or big data. This process ensures complete data management by finding information such as hidden data, unknown correlations and customer preferences. Big data is collected from various sources such as videos, social networks, images and sensors. This technology is widely used to process massive data in 6G systems.

Large Intelligent Surface (LIS)

In the case of THz band signals, there may be many shadow areas due to obstructions due to strong linearity. By installing LIS near these shadow areas, LIS technology that expands the communication area, strengthens communication stability, and provides additional additional services becomes important. An LIS is an artificial surface made of electromagnetic materials and can change the propagation of incoming and outgoing radio waves. LIS can be seen as an extension of massive MIMO, but its array structure and operating mechanism are different from massive MIMO. LIS also has low power consumption in that it operates as a reconfigurable reflector with passive elements, i.e. it only passively reflects signals without using an active RF chain. There are advantages to having In addition, since each passive reflector of the LIS must independently adjust the phase shift of an incident signal, it may be advantageous for a wireless communication channel. By properly adjusting the phase shift through the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

<General terahertz (THz) wireless communication>

THz wireless communication uses wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1THz = 10 ¹² Hz), and refers to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or more. can THz waves are located between RF (Radio Frequency)/millimeter (mm) and infrared bands, and (i) transmit non-metal/non-polarizable materials better than visible light/infrared rays, and have a shorter wavelength than RF/millimeter waves and have high straightness. Beam focusing may be possible. In addition, since the photon energy of the THz wave is only a few meV, it is harmless to the human body. A frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band with low propagation loss due to molecular absorption in the air. Standardization discussions on THz wireless communication are being discussed centering on the IEEE 802.15 THz working group in addition to 3GPP, and standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) can specify or supplement the content described in this specification. there is. THz wireless communication may be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, and the like.

13 is a diagram showing an example of a THz communication application.

As shown in FIG. 13, THz wireless communication scenarios can be classified into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle connections and backhaul/fronthaul connections. In micro networks, THz wireless communication is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading. It can be.

Table 2 below is a table showing an example of a technique that can be used in THz waves.

Transceivers Device Available immature: UTC-PD, RTD and SBD Modulation and coding Low order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phased array with low number of antenna elements Bandwidth 69GHz (or 23GHz) at 300GHz Channel models Partially Data rate 100 Gbps outdoor deployment No Free space loss High Coverage Low Radio Measurements 300GHz indoor Device size Few micrometers

THz wireless communication can be classified based on the method for generating and receiving THz. The THz generation method can be classified as an optical device or an electronic device based technology. 14 is a diagram showing an example of an electronic device-based THz wireless communication transceiver. A method of generating THz using an electronic device includes a method using a semiconductor device such as a resonant tunneling diode (RTD), a local oscillator, and a multiplier. There are a method using a method using a compound semiconductor HEMT (High Electron Mobility Transistor) based integrated circuit, an MMIC (Monolithic Microwave Integrated Circuits) method using a compound semiconductor HEMT (High Electron Mobility Transistor) based integrated circuit, and a method using a Si-CMOS based integrated circuit. In the case of FIG. 14, a doubler, a tripler, or a multiplier is applied to increase the frequency, and the signal is radiated by the antenna after passing through the subharmonic mixer. Since the THz band forms high frequencies, a multiplier is essential. Here, the multiplier is a circuit that makes the output frequency N times greater than the input, matches the desired harmonic frequency, and filters out all other frequencies. In addition, beamforming may be implemented by applying an array antenna or the like to the antenna of FIG. 14 . 14, IF denotes an intermediate frequency, tripler and multipler denote a multiplier, PA denotes a power amplifier, LNA denotes a low noise amplifier, and PLL denotes a phase-locked circuit (Phase -Locked Loop). 15 is a diagram illustrating an example of a method for generating a THz signal based on an optical device, and FIG. 16 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical device.

Optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. An optical element-based THz signal generation technology is a technology that generates an ultra-high speed optical signal using a laser and an optical modulator and converts it into a THz signal using an ultra-high speed photodetector. Compared to a technique using only an electronic device, this technique can easily increase the frequency, generate a high-power signal, and obtain a flat response characteristic in a wide frequency band. As shown in FIG. 15, a laser diode, a broadband light modulator, and a high-speed photodetector are required to generate a THz signal based on an optical device. In the case of FIG. 15 , a THz signal corresponding to a wavelength difference between the lasers is generated by multiplexing light signals of two lasers having different wavelengths. In FIG. 15, an optical coupler refers to a semiconductor device that transmits an electrical signal using light waves in order to provide electrical isolation and coupling between circuits or systems, and UTC-PD (Uni-Traveling Carrier Photo- Detector is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons through bandgap grading. UTC-PD is capable of photodetection above 150 GHz. 16, EDFA (Erbium-Doped Fiber Amplifier) represents an erbium-added optical fiber amplifier, PD (Photo Detector) represents a semiconductor device capable of converting an optical signal into an electrical signal, and OSA represents various optical communication functions (photoelectric conversion, electric light conversion, etc.) represents an optical module (Optical Sub Assembly) that is modularized into one component, and DSO represents a digital storage oscilloscope.

The structure of the photoelectric converter (or photoelectric converter) will be described with reference to FIGS. 17 and 18 . 17 illustrates a structure of a photonic source-based transmitter, and FIG. 18 illustrates a structure of an optical modulator.

In general, a phase or the like of a signal may be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing electrical characteristics through a microwave contact or the like. Accordingly, the optical modulator output is formed as a modulated waveform. A photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal, an O/E conversion by a photoconductive antenna, and a bundle of electrons in light flux. THz pulses can be generated according to emission from relativistic electrons, etc. A THz pulse generated in the above manner may have a unit length of femto second to pico second. An O/E converter uses non-linearity of a device to perform down conversion.

Considering the THz spectrum usage, it is necessary to use several contiguous GHz bands for fixed or mobile service for terahertz systems. more likely to use According to outdoor scenario criteria, available bandwidth may be classified based on oxygen attenuation of 10^2 dB/km in a spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of several band chunks may be considered. As an example of the framework, if the length of a THz pulse for one carrier is set to 50 ps, the bandwidth (BW) becomes about 20 GHz.

Effective down conversion from the IR band to the THz band depends on how to utilize the nonlinearity of the O/E converter. That is, in order to down-convert to the desired terahertz band (THz band), the photoelectric converter (O / E converter) having the most ideal non-linearity to move to the corresponding terahertz band (THz band) design is required. If an O/E converter that does not fit the target frequency band is used, there is a high possibility that an error will occur with respect to the amplitude and phase of the corresponding pulse.

In a single carrier system, a terahertz transmission/reception system may be implemented using one photoelectric converter. Although it depends on the channel environment, as many photoelectric converters as the number of carriers may be required in a multi-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to a plan related to the above-mentioned spectrum use, the phenomenon will be conspicuous. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame). The frequency domain of the specific resource domain may include a plurality of chunks. Each chunk may consist of at least one component carrier (CC).

<Quantum cryptographic communication>

In 6G, securing security technology that can guarantee the safety and confidentiality of user information, which has a vast and diverse structure compared to 5G, is being raised as a major issue. In the 5G communication network, the safety of information has been secured mainly through methods based on mathematical complexity such as the RSA cryptographic system, but the recent rapid development of big data, AI technology, and quantum computing technology threatens absolute safety. As one of the solutions to this, Quantum key distribution (QKD) technology, which applies representative characteristics of quantum mechanics such as non-reproducibility, uncertainty, and entanglement, is being considered as a security technology instead of the existing method of increasing mathematical complexity. In the 5G communication network, the QKD quantum cryptographic communication technology was mainly applied as a security technology for data transmitted through a wired communication network, but in the 6G (wireless communication) system, as well as the existing wired communication network, satellite, V2X, A2G (G2A), It is expected that quantum cryptography communication technology can be expanded and applied to various communication environments such as indoor and underwater.

Among them, satellite communication can be expected as a representative application field of quantum cryptography communication technology. In 6G, it is expected to integrate with satellites to serve the global mobile population. Therefore, integrating terrestrial, satellite, and public networks into one wireless communication system is very important in 6G, and at this time, satellite-based wireless quantum cryptography communication technology is required to secure the safety and reliability of data transmitted between satellites or between satellites and the ground. this may apply. In this regard, since the 2010s, research using satellites to transmit information through a quantum cryptographic communication system between two points at a distance of thousands of km has been conducted, and successful cases of quantum cryptographic communication experiments have been reported, mainly in North America, Europe, and China. there is.

Also, in 6G, as mentioned above, the connectivity of wireless communication is expected to increase rapidly. At this time, new types of devices such as airplanes, ships, self-driving cars, drones, and remotely controlled robots, which are devices to which wireless communication will be applied, will appear. . Therefore, in 6G, since the area of terminals and services is expanded and the capacity of important data such as personal information and confidential information increases, the possibility of intrusion by hackers can become a great threat to communication with the device requiring a high-precision network. As a result, the need for a stronger system than the existing security system is raised in the above field, and accordingly, research on the applicability of quantum cryptographic communication with various devices is being actively conducted.

In addition, as a security technology between devices constituting WiFi and IOT networks that can be mainly applied in indoor environments such as smart offices/homes, quantum cryptographic communication is expected to be of great value as a technology that can ensure safety between large-scale connected devices. do.

Quantum Key Distribution (QKD) technology is a technology that generates secret symmetric key information required for encryption and decryption of transmission data as described in the figure below. To this end, in quantum cryptography communication technology, first, quantum key information is transmitted from a transmitter to a receiver through a quantum channel to share key information. However, in this process, shared key information between transceivers cannot be perfectly matched due to the presence of an eavesdropper, imperfections of components and quantum channels, and the like. Therefore, a post-processing process is performed to correct this, and after exchanging additional information necessary in the post-processing process through a public channel, the key information of the transmitter and receiver is matched based on this information and used as the final secret symmetric key.

19 schematically illustrates an example of quantum cryptographic communication.

According to FIG. 19 , a quantum key distribution (QKD) transmitter 1910 may perform communication by being connected to a QKD receiver 1920 through a public channel and a quantum channel.

At this time, the QKD transmitter 1910 may supply the secret key to the encryptor 1930, and the QKD receiver 1920 may also supply the secret key to the decryptor 1940. Here, plain text may be input/output to the encryptor 1930, and the encryptor 1930 may transmit data encrypted with a secret symmetric key (via an existing communication network) to the decryptor 1940. . In addition, plain text may be input/output to the decoder 1940.

A more detailed description of quantum cryptographic communication is as follows.

In a quantum cryptographic communication system, a signal is carried using a single photon, which is the smallest unit of light, unlike conventional communication methods that communicate by wavelength or amplitude. While stability of conventional cryptosystems is mostly guaranteed by the complexity of mathematical algorithms, quantum cryptographic communication is guaranteed stability as long as the physical laws of quantum mechanics are not broken because it is based on the unique properties of quantum.

For example, in the BB84 protocol, information on states such as polarization and phase of photons is carried, and a secret key can be shared with absolute safety in theory by using the characteristics of both.

Hereinafter, an example of the BB84 protocol in which a secret key is generated by loading information on a polarization state between Alice at the sending side and Bob at the receiving side is shown, and the overall flow of the BB84 protocol for this is as follows.

(1) Alice randomly generates bits.

(2) A transmission polarizer is randomly selected to determine which polarization to carry bit information on.

(3) A polarization signal corresponding to the randomly generated bit in (1) and the randomly selected polarizer in (2) is generated and transmitted through the quantum channel.

(4) Bob randomly selects a measurement polarizer to measure the polarization signal transmitted by Alice.

(5) Measure and store the polarization signal transmitted by Alice with the selected polarizer.

(6) Alice and Bob share which polarizer they used through the classical channel.

(7) A secret key is obtained by storing only bits using the same polarizer and removing bits using different polarizers.

Bits created by Alice 0 One One 0 One 0 0 One Alice's choice of transmission polarizer + + x + x x x + Polarized signal transmitted by Alice ↑ → ↘ ↑ ↘ ↗ ↗ → Bob's choice of measurement polarizer + x x x + x + x Polarization signal measured by Bob ↑ ↗ ↘ ↗ → ↗ → → Verification of whether transmission polarizer and measurement polarizer match Data exchange over classical channels Finally generated secret key 0 One 0 One

Theoretically, this BB84 protocol guarantees absolute security, but there are defects in real hardware implementation, and polarization distortion due to birefringence of an optical fiber is a representative example. Birefringence refers to a phenomenon in which a polarization component perpendicular to an optical axis of the medium and a polarization component horizontal to the optical axis experience different time delays when light passes through a non-isotropic medium. These different time delays cause a phase difference between the two components, and the phase difference between the two components means that the polarization is distorted.

<QBER estimation method in QKD system>

As shown in FIG. 19, the QKD (Quantum Key Distribution) system encrypts and decrypts information to be transmitted and quantum key distribution (QKD) that generates secret symmetric key information used to encrypt and decrypt encrypted information in the encryptor and decryptor. ) can be divided into parts. To generate a secret symmetric key, the QKD part first shares the key information by transmitting quantum key information generated through polarization or phase coding through a quantum channel. However, in this process, shared key information between transceivers cannot be perfectly matched due to the existence of an eavesdropper, component devices, and incompleteness of quantum channels. Therefore, a post-processing process for correcting this is absolutely necessary, and after performing the post-processing process through an open channel, a final secret symmetric key is generated. The post-processing process of quantum cryptography communication technology consists of three steps: estimation of the error rate of quantum key information, quantum key error recovery, and secrecy amplification.

In this specification, the error rate estimation step of quantum key information, which is the first step, is dealt with. In the case of the widely used BB84 protocol, 11% is the standard), the security of the quantum cryptographic communication process through shared key information can be guaranteed, so the cryptographic communication process continues, otherwise, the possibility of a third eavesdropper exists Since this is large, the key information is discarded and the key sharing process is performed again.

As such, the QBER estimation process is a process of determining whether key information shared in quantum cryptographic communication techniques is eavesdropped or not, so high accuracy of QBER, which is the standard, must be guaranteed. However, in order to obtain higher accuracy, information related to more keys must be shared through public channels, which causes a problem in that the amount of leaked information increases. Since an increase in the amount of leaked information amplifies the possibility of direct inference of key information, it is important to minimize the amount of leaked information in a possible quantum cryptographic communication system. Therefore, since there is a trade-off between accuracy and leakage information in the QBER estimation step, a method for securing high QBER estimation accuracy while reducing the leakage information as much as possible is required.

20 schematically illustrates an example of a random sampling technique.

Among existing QBER estimation techniques, a representative technique is a random sampling (RS) technique. As shown in FIG. 20, the RS technique generates only the key information at the position where the basis matches among the quantum key information shared by the transmitter (Alice side) and the receiver (Bob side) through the quantum channel. ) After randomly selecting some (10-20%) of the key(s), a method of estimating QBER by comparing selected transmission/reception sifted key information is used.

The detailed process of the RS technique proceeds as follows.

(1) Randomly selects 10-20% of the sifted key information of the transmitter (Alice side).

(2) The key information and corresponding location information selected by the transmitter are sent to the receiver (Bob side) through an open channel.

(3) In the receiver, QBER e_sift^est is obtained by comparing the received key information of the location information received from the transmitter with the key information of the transmitter, and the equation is as follows.

QBER=e_sift^est=(Number of mismatched key values at the same position among randomly selected sifted keys in the transceiver/receiver)/(Number of randomly selected sifted keys in RS)

(4) All sifted key information used for QBER measurement may be leaked to a third party through public channels, so all discarded, and the remaining post-processing process is performed with the remaining sifted key information. do.

The RS method is known as a method capable of estimating a generally accurate QBER regardless of the error rate occurring in an actual quantum cryptography system. In order to prevent the risk of key information being exposed by a third party, it is absolutely necessary to discard the exposed information. Therefore, a large loss of key information to be used in the final quantum cryptographic communication system occurs in this process. That is, when the RS technique is used as a QBER estimation technique in a QKD system, 10 to 20% of all sifted keys are discarded in this process, which causes the key rate of quantum keys to decrease.

21 schematically shows the overall process and key length change of the QKD system.

As shown in FIG. 21, the QKD system loses a large percentage of the key information generated in the initial stage through several stages of post-processing after quantum key generation, so even the QKD system with the highest key rate can achieve a processing speed of several Mbps. It is known to have Therefore, in order for QKD technology to be used as a technology that guarantees the unconditional safety of information having a data rate of Tbps in the future in 6G, it is necessary to increase the speed of quantum key generation so that it can be used as OTP (One Time Password).

22 schematically illustrates an example of a parity comparison technique.

Therefore, a parity comparison method (PCM), a QBER estimation technique that can prevent the key rate degradation of the QKD system, has emerged. As shown in FIG. 22, the parity comparison technique combines L key information among sifted keys to create one group, and the transmission and reception unit obtained by the modulo 2 sum of each group. After calculating the parity value of 1 bit, this value is shared through a public channel, so there is no direct exposure of key information, so no loss of key information occurs during this process. Finally, the ratio of mismatched parities is calculated through comparison of shared parity values, and QBER is estimated from this value. The detailed QBER estimation process of PCM is as follows.

(1) N parity values are obtained by grouping L sifted keys of the transmitter, and one parity bit value is obtained through the following equation.

(2) The receiving unit obtains n parity bit values through the same process as the transmitting unit.

(3) After transmitting n pieces of parity information from the transmitter to the receiver through the public channel, parity values at the same location among the parity information of the shared transmitter/receiver are compared to determine the parity error probability (i.e., e_parity^est ) is obtained.

e_parity^est = (Number of non-matching parity information of transceiver/receiver)/n

(4) The QBER is derived from the statistically obtained parity error probability e_parity^est and the length L of the group.

QBER=e_sift=(1-(1-2e_parity^est)^(1/L))/2 with statistical e_parity^est and preset L

Proof) The probability that one group (consisting of L sifted keys) has different parity values is equal to the probability that an odd number of errors occur in parity. Therefore, the parity error probability (ie, e_parity^est) can be defined as follows.

이 등가임을 증명하는 과정은 다음과 같다.The right term (1-(1-2e_sift)^L)/2 and the left term

The process of proving this equivalence is as follows.

임을 이용하면 아래와 같이 정리될 수 있다.Substitute y = e_sift, x = 1 - e_sift,

Using , it can be arranged as follows.

At this time, if e_parity^est=(1-(1-2e_sift)^L)/2 is rearranged for e_sift, it is equivalent to e_sift=(1-(1-2e_parity^est)^(1/L))/2.

Since the PCM technique estimates the QBER based on parity information obtained from a set of a certain number of sifted keys rather than the sifted keys of the transceiver, the amount of information leaked through the public channel is smaller than that of the RS technique. And, since the selected key information is not disclosed as a public channel, some of the key information is not discarded. Therefore, it has the advantage that there is no loss of sifted key information.

However, the PCM technique causes a large deviation in QBER estimation accuracy depending on the set value of the group length L. The lower the error rate included in the QKD system, the lower the possibility of including multiple errors even if the length L of the group is long, so sufficient error detection is possible through parity comparison. Since the possibility of including errors increases, it is difficult to estimate the error rate with sufficient accuracy only by parity comparison.

In particular, in PCM, when an even number of errors occur in a group as shown in red in FIG. 22, parity values of transceivers are the same during 1-bit parity comparison, so it is not possible to determine whether or not an error exists. Therefore, as the error rate included in the QKD system increases, the proportion of groups containing an even number of errors increases, so the QBER estimation accuracy of the entire QKD system deteriorates.

23 schematically shows QBER estimation accuracy according to random sampling (TM) and actual QBER of PCM.

23 shows that the length of the sifted key shared through the quantum channel in the quantum cryptosystem is 5000 bit (s), the rate of random sampling is 10% of the total sifted key, and the PCM When the group length (length) L = 15, the QBER estimation accuracy of the two existing techniques is shown as a simulation result performed according to the error rate of the sifted key. The QBER estimation accuracy is obtained through Δ=|e_sift^est-e_sift|, which is the difference between the error rate e_sift^est estimated by each technique and the actual error rate e_sift.

In the case of the PCM technique, it has better accuracy than random sampling at an error rate of 6% or less, and the accuracy deteriorates above that. When an error of more than 6% occurs, the possibility of multiple errors increases and errors that cannot be detected in the PCM parity comparison process increase. However, in terms of leakage information, the PCM technique uses only 333 bits of parity information as leaked information, so it can be seen that it has an advantage over RS that uses 500 bits of key information as leaked information.

Hereinafter, the present specification will be described in more detail.

This specification is a post-processing process (Quantum bit error rate (QBER) estimation, key information recovery, and confidentiality amplification process after exchanging quantum keys in a quantum key distribution (QKD) system among quantum secure communication techniques). In the QBER estimation process, which is the first step of configuration), it relates to a method for improving QBER estimation accuracy without key rate loss of quantum key information. More specifically, from additional information exposed through public channels in the QBER estimation process of estimating the error rate of key information to determine whether the quantum cryptographic key information shared through quantum channels in the QKD system is eavesdropped by a third party. We deal with QBER estimation techniques and devices through syndrome comparison that can improve QBER estimation accuracy compared to existing QBER estimation techniques while ensuring high security of quantum keys.

In the present specification, a syndrome-based method capable of improving the accuracy of a technique for estimating QBER without loss of key information due to exposure of sifted key information to a public channel in the post-processing process of the above quantum key distribution technology Covers QBER estimation techniques.

In the parity-based QBER estimation technique, which is a technique for estimating QBER without loss of existing key information, it is possible to estimate that an error exists in a group through parity comparison of transmission and reception only when an odd number of errors are included in a group. However, when an even number of errors are included in a group, the presence of errors cannot be determined because the parity bits of the transmitter/receiver unit have the same value. Therefore, as the percentage of errors included in the sifted key increases, the probability of occurrence of an even number of errors increases, resulting in a problem in which the accuracy of the parity-based QBER estimation method gradually deteriorates.

The current wired channel-based QKD system is found to have a low error rate within 3% (up to 7%) if there is no eavesdropping by a third party, but the wireless channel-based QKD system is expected to be highly applicable in 6G in the future. In the case of the wired optical channel, implementation results have been reported to have a higher error rate of less than 9% due to channel environment instability, etc., compared to wired optical channels. In addition, QBER, the criterion for interception of quantum key information due to a third party, also uses the BB84 protocol. It is 11% when used, but in the case of other protocols, there are cases where a higher QBER is used as a criterion. Therefore, it is difficult to accurately estimate the error in the case where the error rate of the sifted key is high only with the existing PCM technique.

In this specification, we propose a syndrome comparison method and apparatus that can guarantee high accuracy of QBER estimation up to the criterion for determining eavesdropping by a third party in the QKD system without exposing key information of the QKD system through an open channel. To this end, in the above specification, a syndrome comparison technique is used instead of parity comparison of the existing transceiver unit as a basis for determining whether a group consisting of a set of sifted key information of a certain length contains an error.

Hereinafter, for a better understanding of the examples of the present specification, the disclosure of the present specification will be described through drawings. The following drawings are made to explain a specific example of the present specification. Since the names of specific devices or names of specific signals/messages/fields described in the drawings are provided as examples, the technical features of the present specification are not limited to the specific names used in the drawings below.

24 is a flowchart of a method for estimating an error rate according to an embodiment of the present specification.

According to FIG. 24, the device may receive first syndrome information from another device (S2410). Here, the device may mean a device on the Bob's side, and the other device may mean a device on the Alice's side. In addition, the device may receive selection key information from other devices. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may derive second syndrome information based on the selection key information and the parity check matrix (S2420). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

Meanwhile, the first syndrome information may be expressed as S_A^(i), which will be described later, and the first syndrome information may include S_A^(o(i)) and S_A^(e(i)), which will be described later. can In addition, the second syndrome information may be expressed as S_B^(i), which will be described later, and the second syndrome information may include S_B^(o(i)) and S_B^(e(i)), which will be described later. can Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may detect an odd number of errors and an even number of errors related to the selection key information based on the first syndrome information and the second syndrome information (S2430). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The apparatus may estimate the error rate based on the odd number of errors and the even number of errors (S2440). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

For example, according to claim 1, the device may receive the first syndrome information through a public channel between the device and the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

For example, the device may receive the selection key information through a quantum channel between the device and the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

For example, the selection key information includes a plurality of bits, and the plurality of bits may be grouped into a plurality of bit groups. Here, as an example, each of the first syndrome information and the second syndrome information may be determined in units of the bit group. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

For example, the device may detect the odd number of errors through the first syndrome information and the second syndrome information based on a single bit syndrome comparison. As an example, when the device fails to detect the odd number of errors, it may detect the even number of errors. Here, as an example, the device detects the even number of errors through the first syndrome information and the second syndrome information based on M bit syndrome comparison, and M may be a positive integer. Here, as an example, when the device fails to detect the even number of errors, it may determine that there is no error in the selection key information. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

Meanwhile, another device described above may be, for example, a device including a QKD transmitter (ie, Alice side) and an encoder (and/or decoder). In addition, the device may be a device including a QKD receiver (ie, bob side) and a decoder (and/or encoder).

Examples of the device and other devices may be described as follows through drawings.

25 schematically illustrates an example of a device according to an embodiment of the present specification.

According to FIG. 25 , a quantum key distribution (QKD) transmitter 2510 may perform communication by being connected to a QKD receiver 2520 through a public channel and a quantum channel.

At this time, the QKD transmitter 2510 may supply a secret key to the encrypter 2530, and the QKD receiver 2520 may also supply a secret key to the decryptor 2540. Here, plain text can be input/output to the encryptor 2530, and the encryptor 2530 can transmit data encrypted with a secret symmetric key (through an existing communication network) to the decryptor 2540. . In addition, plain text may be input/output to the decoder 2540.

Here, the encrypter and the decoder can transmit/receive data through the communication network as described above, and the communication network here is, for example, a communication network in the 3GPP series (eg, an LTE/LTE-A/NR based communication network), an IEEE series It may also mean a communication network in .

Meanwhile, the encryptor 2530 and the QKD transmitter 2510 may be included in one device 2550, and the decryptor 2540 and the QKD receiver 2520 may also be included in one device 2560.

For reference, in the drawings, for convenience of explanation, a configuration including only the encryptor 2530 and the QKD transmitter 2510 is illustrated in one device 2550, but the QKD transmitter 2510 is included in the one device 2550 and an encryptor 2530 as well as a separate decryptor may also be included. Similarly, a decoder 2540 and a QKD receiver 2520 as well as a separate encoder may also be included in one device 2560.

Hereinafter, based on the above description, an embodiment of a communication method will be schematically described through drawings.

26 is a flowchart of a method for estimating an error rate according to another embodiment of the present specification.

According to FIG. 26, a device may receive a random access (RA) preamble from another device (S2610). Since a detailed description of this is as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may transmit a random access response (RAR) to the other device in response to the RA preamble (S2620). Since a detailed description of this is as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may perform a radio resource control (RRC) connection procedure with the other device (S2630). Since a detailed description of this is as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may receive data from the other device (S2640). Since a detailed description of this is as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may decrypt data based on the key information (S2650). Here, the key information is determined based on the estimation of the error rate for the selection key information received from the other device, the device receives the first syndrome information from the other device, and based on the selection key information and the parity check matrix Deriving second syndrome information, detecting odd-numbered errors and even-numbered errors related to the screening key information based on the first syndrome information and second syndrome information, and determining the error rate based on the odd-numbered errors and even-numbered errors Based on performing the estimation, an error rate can be estimated. Since a detailed description of this is as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

Hereinafter, specific embodiments of the present specification, which were previously omitted, will be described.

<QBER Estimation Method Based on Syndrome Comparison in QKD System(s)>

27 schematically illustrates a configuration diagram of a group detection technique including odd and even number errors through syndrome comparison of a transceiver.

Similar to the existing parity-based QBER estimation technique, the above specification proposes a method of estimating QBER by detecting whether or not there is an error in a group composed of a certain number of sifted key information. To this end, in the present specification, a group with an odd number of errors and a group with an even number of errors are compared through syndromes obtained by multiplying the same parity check matrix shared in advance in the transceiver unit and a sifted key of a specific group. , to obtain the group error rate in each case, and to obtain the QBER of the entire QKD system from the statistical group error rate.

In the syndrome-based QBER estimation technique presented in this specification, a parity check matrix H^m (m≥3) of the same type to be used for syndrome operation using a Hamming hash function before key information sharing through quantum channels is divided in the transceiver, and its form is as follows. Here, h_((i, j)) represents the value of (i, j) of H^m. And the last column is configured to have all zeros as elements. This serves to reduce the amount of leaked information by extending the length of the group from 2^m-1 to 2^m.

Here, H^m can be arranged as follows.

In the existing channel coding technique, the position of the bit where the error occurred was found based on the syndrome obtained using H^m. It is used to distinguish between a group with an even number of errors and a group without errors by checking whether they match.

In addition, in order to detect a group containing an odd number of errors, a matrix form in which all the following elements are 1 is additionally used in the present specification. In this specification, it is possible to check whether an odd number of errors are included in the same group of the transmitting/receiving unit by using H_e^m.

From this, in the above specification, the parity check matrix of the following form is commonly shared and used in both sides for syndrome comparison of the transceiver.

In the syndrome comparison-based QBER estimation technique, a group including an error can be detected through the system configuration shown in FIG. 27 using H_A^m, and the entire QBER estimation process proceeds as follows.

Here, H_A^m can be arranged as follows.

(1) Detects the number of groups in which an odd number of errors exist due to the syndrome mismatch among the entire group by multiplying the sifted key value of the group consisting of 2^m bits by H_e^m to obtain a 1-bit syndrome .

(2) In the case of a group without an odd number of errors, the m bit(s) syndrome is obtained by multiplying the sifted key value of the group consisting of 2^m bits by H^m to obtain an even number of errors among the entire group. Detect the number of groups in which errors exist. At this time, among the groups in which an even number of errors are detected, a group in which it is determined that no errors exist due to coincidence of the syndromes of the transceiver is excluded.

(3) Calculate the QBER of the entire QKD system using the results of the above two processes.

Methods for detecting groups containing odd and even numbers of errors are as follows.

One. How to detect groups with an odd number of errors

FIG. 28 schematically shows an example (m=3) of detecting a case where an odd number of group errors occur using a 1-bit syndrome.

When an odd number of errors occur, the transmitter/receiver individually obtains a syndrome of m+1 bits by using the product of the entire parity check matrix H_A^m and a group consisting of 2^m-bit sifted keys, and then checks whether they match. It is possible to determine whether an error exists within a group shared between the two groups.

However, in the case of an odd number of errors, if H_e^m is used, it is possible to check the existence of odd errors in a group only with a 1-bit syndrome.

For example, in the case of m = 3 as shown in FIG. 28, when comparing sifted keys in the same group of the transmitter and receiver, if an odd number of errors occur in the group, a parity check matrix in which all elements are 1 It can be seen that the product of and always has a different value. Therefore, a group in which an odd number of errors occur in the receiver can be detected with 100% accuracy only with a 1-bit syndrome.

29 shows an entire process of detecting a group including an odd number of errors, and a process of estimating whether or not an odd number of errors are included is as follows.

(1) When the total length of the quantum key generated by the transmitter (Alice) is n, it is divided into k groups. Therefore, the length of one group can be set to n/k. When the quantum key information of the ith group generated by the transmitter is X_A^i, the key information X_B^i received from the receiver through the quantum channel is perfectly consistent with X_A^i because some errors occur due to system imperfections. I never do that.

(2) In order to estimate the error rate of the key information divided by the transmitter and receiver, the transmitter obtains the 1-bit syndrome S_A^(o(i)) through the product of H_e^m and the key information X_A^i of the ith transmitter group. , it is sent to the receiver through the public channel.

(3) After obtaining the 1-bit syndrome S_B^(o(i)) through the product of H_e^m and the key information X_B^i of the ith transmitter group in the receiver, S_A^(o(i)) and S_B^(o Check whether (i)) matches.

(4) If the two syndromes do not match, it is determined that an odd number of errors are included in the i-th group.

(5) After repeating the above process for all k groups, the group error probability e_syndrome^odd containing an odd number of errors is obtained.

e_syndrome^odd=(Number of groups judged to contain an odd number of errors)/k

(6) From e_syndrome^odd, the error rate QBER_odd of the sifted key information of the group containing an odd number of errors is obtained from the following equation.

QBER_odd=e_sift=(1-(1-2e_syndrome^odd )^(1/(2^m)))/2

The proof process of the QBER_odd equation is as follows.

The probability e_syndrome^odd of an odd number of errors in a group of receivers can be expressed as the sum of probabilities p_odd in each situation in which an odd number of errors occur. Here, e_sift means the error rate that occurs in one bit of key information, that is, QBER.

And from the above expression, p_odd = (1-(1-2e_sift)^(2^m))/2 can be shown as follows.

에 y=e_(sift), x=1-e_sift, L=2^m을 대입하면 다음과 같이 정리할 수 있다.Binomial distribution

By substituting y=e_(sift), x=1-e_sift, and L=2^m into

Therefore, it can be defined as e_syndrome^odd=(1-(1-2e_sift)^(2^m))/2. If this is rearranged for e_sift again, the QBER of the group containing an odd number of errors can be obtained, which is as follows.

QBER_odd=e_sift=(1-(1-2e_syndrome^odd )^(1/(2^m)))/2 with statistical e_syndrome^odd and preset 2^m

2. Method for detecting groups with an even number of errors

FIG. 30 schematically illustrates an example (m=3) of detecting a case in which an even number of group errors occur using a syndrome.

In the process of determining the odd number of errors, in the case of a group in which both syndromes have the same value in the 1-bit syndrome comparison process of the transceiver, an odd number of errors are not included in the group, so a syndrome comparison process to check whether an even number of errors is included is additionally performed. do. In this process, by using the product of the parity check matrix H^m and a group consisting of a 2^m-bit sifted key, the transmitter/receiver individually obtains m-bit syndromes, checks whether they match, and shares the group between the transmitter/receiver. It is possible to determine whether an even error exists within

For example, in the case of m = 3 as shown in FIG. 30, when comparing sifted keys in the same group of the transmitter and receiver, if an even number of errors occur in the group, the multiplication with the parity check matrix H^m is different. It can be seen that it has a value, and through this, a group containing even errors can be detected.

31 shows an entire process of detecting a group including an even number of errors, and a process of estimating whether an even number of errors is included is as follows.

(1) The receiving unit transmits the position information of the group determined not to contain an odd number of errors to the transmitting unit through the public channel.

(2) When X_A^i is the quantum key information of the i-th group that does not contain an odd number of errors in the transmitter, m-bit syndrome is generated through the product of H^m and the key information X_A^i of the i-th transmitter group in the transmitter After obtaining S_A^(e(i)), it is sent to the receiver through the public channel.

(3) In the receiver, after obtaining the m bit syndrome S_B^(e(i)) through the product of H^m and the key information X_B^i of the ith transmitter group, S_A^(e(i)) and S_B^(e Check whether (i)) matches.

(4) If the two syndromes do not match, it is determined that the i-th group contains two or more even-numbered errors, and otherwise, it is determined that no errors exist in the group.

(5) After the above process is repeated for all groups determined to contain an even number of errors, the error probability e_syndrome^even for groups containing an even number of errors is obtained, excluding the groups without errors.

e_syndrome^even=(Number of groups determined to contain an even number of errors, excluding groups without errors)/(k)

(6) The error rate QBER_even of the sifted key information of the group containing an even number of errors from e_syndrome^even is obtained from the following equation.

QBER_even=e_sift=(1+(1-2e_syndrome^odd)^(1/(2^m)))/2

The proof process of the QBER_odd equation is as follows.

The probability e_syndrome^even of an even number of errors in a group of receivers can be expressed as the sum of probabilities p_even in each situation in which an even number of errors occur. Here, e_sift means the error rate that occurs in one bit of key information, that is, QBER.

And from the above expression, p_even = (1+(1-2e_sift)^(2^m))/2 can be shown as follows.

에 y=e_(sift), x=1-e_sift, L=2^m을 대입하면 다음과 같이 정리할 수 있다.Binomial distribution

By substituting y=e_(sift), x=1-e_sift, and L=2^m into

Therefore, it can be defined as e_syndrome^even=(1+(1-2e_sift)^(2^m))/2. If this is rearranged for e_sift again, the QBER of the group including an even number of errors can be obtained, which is as follows.

QBER_even=e_sift=(1+(1-2e_syndrome^odd)^(1/(2^m)))/2 with statistical e_syndrome^even and preset 2^m

However, the above QBER_even also includes the case where there is no error in the group, so the ratio for this case should be excluded. Therefore, the QBER in the case where two or more even-numbered errors are included in a group can be expressed as the following equation. In this equation, the # of even error group(s) represents the proportion of groups containing two or more even errors, and the # of no error group(s) represents the proportion of groups containing no errors.

with statistical e_syndrome^odd and preset 2^m

3. Overall QBER estimation method

32 schematically illustrates an example of a syndrome-based QBER estimation method in a QKD system.

In the syndrome-based QBER estimation technique, first, as shown in FIG. 32, the ratio of a group with an odd number of errors and a group with an even number of errors is individually obtained through syndrome comparison at the same group location of the transceiver.

The total QBER (i.e., QBER_total) is estimated from QBER_odd, which is obtained from the proportion of sifted key groups estimated to have an odd number of errors, and QBER_even, which is obtained from the proportion of groups estimated to have an even number of errors, and the expression is As follows. Here, since the ratios of groups containing odd and even errors are different, an accurate QBER cannot be obtained by simply summing the QBERs of groups containing odd and even errors. Therefore, the QBER of the entire QKD system is obtained by multiplying the QBER of each case by the occurrence rate and then summing it.

- # of odd error group(s): the number of groups for which it is determined that an odd number of errors exist

- # of even error group(s): the number of groups in which it is determined that an even number of errors exist (excluding groups in which it is determined that no errors exist)

- e_syndrome^even: Proportion of groups with an even number of errors (excluding groups judged to have no errors)

- e_syndrome^odd: Proportion of groups with an odd number of errors

According to the above embodiments of the present specification, the following expected effects may be obtained.

In this specification, a method for solving the problem of a method of estimating the QBER of an existing quantum cryptography communication system through syndrome comparison obtained from a quantum key selected by a transceiver is proposed. In addition, in order to verify the effect of the specification technique compared to the existing one, in the present specification, first, the group length and the amount of leaked information are matched, and then the QBER estimation accuracy / leaked information amount / sifted key rate / safety (of the key information from the leaked information) Restoration possibility) to verify the effect of the above specification. In addition, the effect of improving the QBER estimation accuracy according to the increase in the length of the entire sifted key is analyzed.

One. Performance comparison based on equal group length

The effect of the proposed method compared to the existing method was compared in the case where the length of the entire sifted key was 256 bit(s). Here, the performance is compared after matching the group length used in the existing PCM and specification techniques.

A. Comparison of QBER Estimation Accuracy

33 to 35 schematically show QBER estimation accuracy comparisons in three group lengths.

33 to 35 show the results of comparing the QBER estimation accuracy of the specification technique and the existing two techniques in three group lengths of 8/16/32 bits.

Existing techniques 1. Random sampling (RS) technique: 12.5% of all sifted keys are used for QBER estimation and discarded

Existing method 2. PCM (Parity comparison method): Simulation by increasing the parity group size to 8/16/32 bits

specification technique. Simulation by increasing syndrome group size to 8/16/32 bits

In the case of 100% investigation (Ideal): When the accuracy of QBER estimation is 100% (when the QBER of all quantum key information is checked)

As can be seen from the results of FIGS. 33 to 35, the QBER estimation technique of this specification has higher accuracy in all three group lengths than the PCM technique in the same group length, and the longer the group length, the greater the accuracy improvement effect. The QBER estimation technique used in most QKD systems uses the BB84 protocol. At this time, since the criterion for determining whether or not wiretapping by a third party is QBER 11%, high accuracy is required at a sifted key error rate within 11%. . In the case of the specification technique, it can be seen that in the QBER of 11% or less, the QBER estimation accuracy is close to the ideal case in which all key information is compared. In terms of accuracy, the RS technique also has good QBER estimation accuracy regardless of the error rate of the sifted key, but a much larger key rate loss due to key information loss occurs compared to the specification technique.

B. Comparison of Leakage Information

In the process of estimating QBER, some information necessary for estimating the error rate needs to be transmitted through an open channel. Therefore, at this time, it is necessary to minimize the amount of leaked information to prevent a third party from inferring entire sifted key information from information leaked through some public channels. Therefore, comparing the amount of leaked information between the proposed method and the existing method, the results are as follows.

- RS technique: 64 bit(s) of information leakage

Key length * sampling rate (12.5%) = 256 * 0.125 = 32 bit(s) of sifted key information leak

32 bit(s) of location information leak of a randomly chosen sifted key

- PCM method: parity comparison of 1 bit per group

Total amount of leak information = number of groups = n bit(s) = 256/group length

Summarizing this through the table, it can be as follows.

group length (amount of leaky information per group) Amount of total parity information leaked 8 (1 bit parity/group) 256/8 = 32 (32 bit(s)) parity information leakage 16 (1 bit parity/group) 256/16 = 16 (16 bit(s)) parity information leakage 32 (1 bit parity/group) 256/32 = 8 (8 bit(s)) parity information leakage

- Specification technique: average 1 bit per group consisting of 2^m length (odd error group search) + m/2 bit (odd error group search) = (m+2)/2 bit syndrome comparison

Total amount of leak information = n ((m+2)/2)

Summarizing this through the table, it can be as follows.

2^m group length (amount of leaky information per group) The total amount of syndrome information leaked 8 (2.5 beat syndrome/group) 256/8 = 32 (80 bit(s)) of syndrome information leakage 16 (3-bit syndrome/group) 256/16 = 16 (48 bit(s)) of syndrome information leakage 32((3.5 beat syndrome/group) 256/32 = 8 (28 bit(s)) of syndrome information leakage

C. Comparison of Key Rate Loss The QKD system can guarantee maximum safety when it can be used as a one time pad by securing a high key rate. Therefore, the key rate loss must be minimized even in the QBER estimation process, which is necessarily included in constructing the QKD system. From this point of view, comparing the key rate loss that occurs in the QBER estimation process of the existing technique and the proposed technique is as follows:

- RS technique. Since 12.5% of the total quantum key information is exposed through public channels, a third party can find out the public key information, so 32 bits of exposed key information among 256 bits of key information are discarded after being used for QBER estimation. Thus, 12.5% of the total sifted quantum key rate is reduced.

- PCM & specification techniques. Instead of directly exposing key information through a public channel, the parity or syndrome obtained from the group is disclosed to prevent direct exposure of key information and key information reduction. Therefore, there is no sifted quantum key rate reduction in the present specification and PCM.

D. Possibility of restoring key information from leaked information

- RS technique. Since 12.5% of the total quantum key information is disclosed through public channels, 12.5% of the total key information is exposed to third parties, and there is a high risk of inferring the entire key information from this information.

8^(-73) (L=32일 때)- PCM. After determining a certain L bit of information among the sifted key information as one group, a one-bit parity value is obtained by performing a modulo 2 operation on all key information in the group. The probability of finding out all the information is 1/2^(L-1). Also, since the total number of groups is 256/L, the probability of finding the entire quantum key information from the entire parity information is (1/2^(L-1))^(256/L)

8^(-73) (when L=32)

8^(-78) (L=32일 때)이므로 안전성이 가장 높다.- Specification techniques. After determining a certain L bit of information among the sifted key information as one group, the syndrome is obtained by multiplying all the key information in the group with the parity check matrix, so that all information of one group can be obtained from the syndrome information of one group. The probability of finding it is 1/2^L. Since the total number of groups is 256/L, the probability of finding the entire quantum key information from the entire parity information is (1/2^L)^(256/L)

8^(-78) (when L=32), so it has the highest safety.

2. Performance comparison based on the same amount of leak information

This time, we compare the performance after matching the amount of information leaked to the public channel in each comparison technique. Here, the case where the length of the entire sifted key is 256 bits is considered.

A. Comparison of leakage information

The performance analysis was conducted by unifying the amount of leakage information to 28 bits in all techniques, and the parameters of each technique for unifying the amount of leakage information are as follows:

- Specification techniques. The syndrome group size is fixed to 32 bit(s).

- PCM technique. The parity group size is fixed at 9 bit(s).

- RS technique. The error rate is estimated by random selection of 10.9% of the total quantum keys.

B. QBER Estimation Accuracy Comparison

36 schematically shows an example of QBER estimation accuracy comparison of each technique based on the same amount of leakage information.

Within the maximum error rate of 11% used for QBER measurement in the QKD technique, despite the fact that the proposed technique uses a group size that is three times larger than that of PCM, the group containing an even number of errors can be detected through the syndrome. It can be seen in FIG. 36 that the QBER estimation accuracy is slightly better. And the RS method shows good accuracy regardless of the error rate of the sifted key.

C. Key Rate Loss Comparison

- RS technique. Since 10.9% of the total quantum key information is exposed through public channels, a third party can find out the public key information. In order to prevent the risk of key information hacking, 28 bits of exposed key information among 256 bits of key information are discarded, so the total sifted quantum key rate is reduced by 10.9%.

- PCM & specification technique. There is no loss of key information because it is a method of disclosing the parity or syndrome obtained from the group instead of directly exposing key information through public channels. Thus, no sifted quantum key rate reduction occurs.

D. Possibility of restoring key information from leaked information

- RS technique. Since 10.9% of the total quantum key information is exposed through public channels, 10.9% of the total key information is exposed to third parties, and there is a high risk of inferring the entire key information from this information.

3^(-69)- PCM. After determining a certain 9-bit information among the sifted key information as one group, a one-bit parity value is obtained by performing a modulo 2 operation on all key information in the group. The probability of finding all the information is 1/2^8, and the total number of groups is 256/9. Therefore, the probability of finding the entire quantum key information from the entire parity information is (1/2^8)^(256/9)

3^(-69)

8^(-78)이므로 기존 기법에 비해 가장 안전성이 높다.- Specification techniques. After determining a certain 32-bit information among the sifted key information as one group, a syndrome is obtained by multiplying all the key information in the group with the parity check matrix pre-divided by the transceiver. The probability of finding out all the information of one group is 1/2^32, and the total number of groups is 256/32. Therefore, the probability of finding the entire quantum key information from the entire parity information is (1/2^32)^(256/32)

Since it is 8^(-78), it is the most safe than the existing techniques.

3. Comparison of QBER estimation accuracy of PCM versus specification techniques according to sifted key length changes

37 schematically shows an example of QBER estimation accuracy comparison of PCM (left) and inventive technique (right) according to sifted key length change.

37 increases the length of the sifted quantum key to 256/512/1024/4096 and shows the result of comparing the QBER estimation accuracy of the existing PCM and specification techniques. And, in the figure, the case of performing a complete survey (Ideal) means the case where the accuracy of QBER estimation is 100%, and the closer the error rate is to the case of total survey (Ideal), the higher the QBER estimation accuracy.

There is always an error in the shared quantum key information due to the imperfection of the QKD system, but if it exceeds QBER11% (based on the BB84 technique), which is the criterion for determining eavesdropping, it is difficult to determine whether the QBER increased due to eavesdropping or a problem in the system. Therefore, if the QBER exceeds 11%, the shared key information is discarded. However, at an error rate below the eavesdropping criterion, data encryption is performed through the shared key information, so determining whether or not the encryption key information has been eavesdropped through accurate QBER estimation is the most important process in securing safety through the QKD system, and high accuracy is guaranteed. It should be. Existing PCM can estimate relatively accurate QBER at an error rate of less than 6%, but the error is large at a larger error rate, so it cannot be used as a reliable QBER estimation technique when a system error of 6% or more occurs. However, since the technique of the present specification detects a group containing an even number of errors that PCM cannot detect, it can be seen that accurate QBER estimation is possible regardless of the quantum key length even at an error rate of less than 10%, which is more extended than before.

Effects obtainable through specific examples of the present specification are not limited to the effects listed above. For example, there may be various technical effects that a person having ordinary skill in the related art can understand or derive from this specification. Accordingly, the specific effects of the present specification are not limited to those explicitly described in the present specification, and may include various effects that can be understood or derived from the technical features of the present specification.

So far, various examples of error rate estimation methods have been described. Hereinafter, a method for estimating an error rate from various subject perspectives will be described.

Hereinafter, for a better understanding of the examples of the present specification, the disclosure of the present specification will be described through drawings. The following drawings are made to explain a specific example of the present specification. Since the names of specific devices or names of specific signals/messages/fields described in the drawings are provided as examples, the technical features of the present specification are not limited to the specific names used in the drawings below.

38 is a flowchart of a method for estimating an error rate, from a (Bob Side's) device perspective, according to an embodiment of the present specification.

The device may receive the first syndrome information from the other device (S3810). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may derive second syndrome information based on the selection key information and the parity check matrix (S3820). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

Based on the first syndrome information and the second syndrome information, an odd number of errors and an even number of errors related to the selection key information may be detected (S3830). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The error rate may be estimated based on the odd number of errors and the even number of errors (S3840). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

39 is a flowchart of a method for estimating an error rate from a (Bob Side's) device perspective, according to another embodiment of the present specification.

The device may receive a random access (RA) preamble from another device (S3910). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may transmit a random access response (RAR) to the other device in response to the RA preamble (S3920). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may perform a radio resource control (RRC) connection procedure with the other device (S3930). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may receive data from the other device (S3940). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may decrypt the data based on the key information (S3950). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The key information may be determined based on the estimation of the error rate for the selection key information received from the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device receives first syndrome information from the other device, derives second syndrome information based on the selection key information and the parity check matrix, and based on the first syndrome information and the second syndrome information, The error rate may be estimated based on detecting an odd number of errors and an even number of errors related to the selection key information, and estimating the error rate based on the odd number of errors and the even number of errors. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

40 is a block diagram of an example of an apparatus for estimating an error rate, from a (Bob Side's) apparatus perspective, according to an embodiment of the present specification.

According to FIG. 40 , the processor 4000 may include a preamble receiver 4010, a RAR transmitter 4020, an RRC connection performer 4030, a data receiver 4040, and a decoder 4050. Here, the processor may be the processor in FIGS. 43 to 53 to be described later.

The preamble receiving unit 4010 may be configured to receive a random access (RA) preamble from another device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The RAR transmitter 4020 may be configured to transmit a random access response (RAR) to the other device in response to the RA preamble. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The RRC connection performing unit 4030 may be configured to perform a radio resource control (RRC) connection procedure with the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The data receiving unit 4040 may be configured to receive data from the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The decryption unit 4050 may be configured to decrypt the data based on key information. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The key information may be determined based on the estimation of the error rate for the selection key information received from the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device receives first syndrome information from the other device, derives second syndrome information based on the selection key information and the parity check matrix, and based on the first syndrome information and the second syndrome information, The error rate may be estimated based on detecting an odd number of errors and an even number of errors related to the selection key information, and estimating the error rate based on the odd number of errors and the even number of errors. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

On the other hand, although not shown separately, the present specification may also include the following embodiments.

According to one embodiment, an apparatus includes a transceiver, at least one memory and at least one processor operably coupled with the at least one memory and the transceiver, the processor comprising random access (RA) from another device. configured to control the transceiver to receive a preamble, and configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble, and radio resource control (RRC) with the other device configured to perform a connection procedure, configured to control the transceiver to receive data from the other device, and configured to decrypt the data based on key information, wherein the key information is selected key information received from the other device is determined based on an estimation of an error rate for , wherein the device receives first syndrome information from the other device, derives second syndrome information based on the selection key information and a parity check matrix, and Based on the syndrome information and the second syndrome information, an odd number of errors and an even number of errors related to the selection key information are detected, and the error rate is estimated based on the odd number of errors and the even number of errors. Thus, it may be an apparatus characterized in estimating the error rate.

According to another embodiment, an apparatus includes at least one memory and at least one processor operably coupled with the at least one memory, the processor comprising: a transceiver to receive a random access (RA) preamble from another apparatus; and configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble, and to perform a radio resource control (RRC) connection procedure with the other device. and configured to control the transceiver to receive data from the other device and to decrypt the data based on key information, wherein the key information is configured to estimate an error rate for selected key information received from the other device. is determined based on, wherein the device receives first syndrome information from the other device, derives second syndrome information based on the selection key information and a parity check matrix, and the first syndrome information and the second syndrome information Based on syndrome information, odd-numbered errors and even-numbered errors related to the selection key information are detected, and the error rate is estimated based on the odd-numbered errors and the even-numbered errors, and the error rate is determined. It may be a device characterized by estimating.

According to another embodiment, in at least one computer readable medium containing instructions based on being executed by at least one processor, RA from another device. (random access) configured to control the transceiver to receive a preamble, configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble, the other device and RRC (radio resource control) connection procedure, configured to control the transceiver to receive data from the other device, and configured to decrypt the data based on key information, wherein the key information is received from the other device determined based on an estimation of an error rate for selection key information, wherein the device receives first syndrome information from the other device and derives second syndrome information based on the selection key information and a parity check matrix; Based on the first syndrome information and the second syndrome information, odd-numbered errors and even-numbered errors related to the screening key information are detected, and the error rate is estimated based on the odd-numbered errors and the even-numbered errors. Based on the performance, it may be a recording medium characterized in that the error rate is estimated.

41 is a flowchart of a method for estimating an error rate, from a device perspective (of Alice Said), according to an embodiment of the present disclosure.

The device may transmit a random access (RA) preamble to another device (S4110). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may receive a random access response (RAR) from the other device in response to the RA preamble (S4120). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device may perform a radio resource control (RRC) connection procedure with the other device (S4130). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device may transmit data to the other device (S4140). Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The device encodes the data based on key information, the device transmits the first syndrome information related to the key information to the other device, and the device transmits selection key information related to the key information to the other device. can be sent to Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

42 is a block diagram of an example of an apparatus for estimating an error rate, from a (Alice Said's) apparatus perspective, according to an embodiment of the present disclosure.

According to FIG. 42 , the processor 4200 may include a preamble transmitter 4210, a RAR receiver 4220, an RRC connection performer 4230, and a data transmitter 4240. Here, the processor may be the processor in FIGS. 43 to 53 to be described later.

The preamble transmission unit 4210 may be configured to transmit a random access (RA) preamble to another device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of explanation.

The RAR receiving unit 4220 may be configured to receive a random access response (RAR) from the other device in response to the RA preamble. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The RRC connection performing unit 4230 may be configured to perform a radio resource control (RRC) connection procedure with the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The data transmitter 4240 may be configured to transmit data to the other device. Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

The device encodes the data based on key information, the device transmits the first syndrome information related to the key information to the other device, and the device transmits selection key information related to the key information to the other device. can be sent to Since more specific details about this are as described above and/or described later, repeated description of overlapping contents will be omitted for convenience of description.

43 illustrates the communication system 1 applied to this specification.

Referring to FIG. 43, a communication system 1 applied to this specification includes a wireless device, a base station, and a network. Here, the wireless device means a device that performs communication using a radio access technology (eg, 5G New RAT (NR), Long Term Evolution (LTE)), and may be referred to as a communication/wireless/5G device. Although not limited thereto, wireless devices include robots 100a, vehicles 100b-1 and 100b-2, XR (eXtended Reality) devices 100c, hand-held devices 100d, and home appliances 100e. ), an Internet of Thing (IoT) device 100f, and an AI device/server 400. For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (eg, a drone). XR devices include Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR) devices, Head-Mounted Devices (HMDs), Head-Up Displays (HUDs) installed in vehicles, televisions, smartphones, It may be implemented in the form of a computer, wearable device, home appliance, digital signage, vehicle, robot, and the like. A portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), a computer (eg, a laptop computer, etc.), and the like. Home appliances may include a TV, a refrigerator, a washing machine, and the like. IoT devices may include sensors, smart meters, and the like. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device 200a may operate as a base station/network node to other wireless devices.

Here, the wireless communication technology implemented in the wireless device of the present specification may include Narrowband Internet of Things for low power communication as well as LTE, NR, and 6G. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2. no. Additionally or alternatively, the wireless communication technology implemented in the wireless device of the present specification may perform communication based on LTE-M technology. At this time, as an example, LTE-M technology may be an example of LPWAN technology, and may be called various names such as eMTC (enhanced machine type communication). For example, LTE-M technologies are 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and/or 7) It may be implemented in at least one of various standards such as LTE M, and is not limited to the above-mentioned names. Additionally or alternatively, the wireless communication technology implemented in the wireless device of the present specification may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low power communication. It may be, and is not limited to the above-mentioned names. For example, ZigBee technology can generate personal area networks (PANs) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called various names.

The wireless devices 100a to 100f may be connected to the network 300 through the base station 200 . AI (Artificial Intelligence) technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI server 400 through the network 300. The network 300 may be configured using a 3G network, a 4G (eg LTE) network, or a 5G (eg NR) network. The wireless devices 100a to 100f may communicate with each other through the base station 200/network 300, but may also communicate directly (eg, sidelink communication) without going through the base station/network. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). In addition, IoT devices (eg, sensors) may directly communicate with other IoT devices (eg, sensors) or other wireless devices 100a to 100f.

Wireless communication/connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 200 and the base station 200/base station 200. Here, wireless communication/connection refers to various wireless connections such as uplink/downlink communication 150a, sidelink communication 150b (or D2D communication), and inter-base station communication 150c (e.g. relay, Integrated Access Backhaul (IAB)). This can be achieved through technology (eg, 5G NR) Wireless communication/connection (150a, 150b, 150c) allows wireless devices and base stations/wireless devices, and base stations and base stations to transmit/receive radio signals to/from each other. For example, the wireless communication/connection 150a, 150b, and 150c may transmit/receive signals through various physical channels.To this end, based on various proposals of the present specification, for transmission/reception of radio signals At least some of various configuration information setting processes, various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.), resource allocation processes, etc. may be performed.

Meanwhile, NR supports a number of numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, when the SCS is 15 kHz, it supports a wide area in traditional cellular bands, and when the SCS is 30 kHz/60 kHz, dense-urban, lower latency and a wider carrier bandwidth, and when the SCS is 60 kHz or higher, a bandwidth greater than 24.25 GHz is supported to overcome phase noise.

The NR frequency band may be defined as a frequency range of two types (FR1 and FR2). The number of frequency ranges may be changed, and for example, the frequency ranges of the two types (FR1 and FR2) may be shown in Table 6 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 may mean "sub 6 GHz range" and FR2 may mean "above 6 GHz range" and may be called millimeter wave (mmW) .

Frequency range designation Corresponding frequency range Subcarrier Spacing FR1 450MHz - 6000MHz 15, 30, 60 kHz FR2 24250MHz - 52600MHz 60, 120, 240 kHz

As described above, the number of frequency ranges of the NR system can be changed. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 7 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included in FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, and may be used, for example, for vehicle communication (eg, autonomous driving).

Frequency range designation Corresponding frequency range Subcarrier Spacing FR1 410MHz - 7125MHz 15, 30, 60 kHz FR2 24250MHz - 52600MHz 60, 120, 240 kHz

Hereinafter, an example of a wireless device to which the present specification is applied will be described. FIG. 44 illustrates a wireless device applicable to the present specification. Referring to FIG. 44, a first wireless device 100 and a second wireless device 200 may transmit and receive radio signals through various radio access technologies (eg, LTE, NR). Here, {the first wireless device 100, the second wireless device 200} is the {wireless device 100x, the base station 200} of FIG. 43 and/or the {wireless device 100x, the wireless device 100x. } can correspond.

The first wireless device 100 includes one or more processors 102 and one or more memories 104, and may additionally include one or more transceivers 106 and/or one or more antennas 108. The processor 102 controls the memory 104 and/or the transceiver 106 and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or flowcharts of operations disclosed herein. For example, the processor 102 may process information in the memory 104 to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 106. In addition, the processor 102 may receive a radio signal including the second information/signal through the transceiver 106, and then store information obtained from signal processing of the second information/signal in the memory 104. The memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102 . For example, memory 104 may perform some or all of the processes controlled by processor 102, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 102 and memory 104 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 106 may be coupled to the processor 102 and may transmit and/or receive wireless signals via one or more antennas 108 . The transceiver 106 may include a transmitter and/or a receiver. The transceiver 106 may be used interchangeably with a Radio Frequency (RF) unit. In this specification, a wireless device may mean a communication modem/circuit/chip.

The second wireless device 200 includes one or more processors 202, one or more memories 204, and may further include one or more transceivers 206 and/or one or more antennas 208. Processor 202 controls memory 204 and/or transceiver 206 and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. For example, the processor 202 may process information in the memory 204 to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206. In addition, the processor 202 may receive a radio signal including the fourth information/signal through the transceiver 206 and store information obtained from signal processing of the fourth information/signal in the memory 204 . The memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202 . For example, memory 204 may perform some or all of the processes controlled by processor 202, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them. Here, the processor 202 and memory 204 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206 may be coupled to the processor 202 and may transmit and/or receive wireless signals via one or more antennas 208 . The transceiver 206 may include a transmitter and/or a receiver. The transceiver 206 may be used interchangeably with an RF unit. In this specification, a wireless device may mean a communication modem/circuit/chip.

Hereinafter, hardware elements of the wireless devices 100 and 200 will be described in more detail. Although not limited to this, one or more protocol layers may be implemented by one or more processors 102, 202. For example, one or more processors 102, 202 may implement one or more layers (eg, functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors 102, 202 may generate one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) in accordance with the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed herein. can create One or more processors 102, 202 may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flow diagrams disclosed herein. One or more processors 102, 202 generate PDUs, SDUs, messages, control information, data or signals (e.g., baseband signals) containing information according to the functions, procedures, proposals and/or methods disclosed herein , can be provided to one or more transceivers 106, 206. One or more processors 102, 202 may receive signals (eg, baseband signals) from one or more transceivers 106, 206, and descriptions, functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein PDUs, SDUs, messages, control information, data or information can be obtained according to these.

One or more processors 102, 202 may be referred to as a controller, microcontroller, microprocessor or microcomputer. One or more processors 102, 202 may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs). may be included in one or more processors 102 and 202. The descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like. Firmware or software configured to perform the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein may be included in one or more processors 102, 202 or stored in one or more memories 104, 204 and It can be driven by the above processors 102 and 202. The descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.

One or more memories 104, 204 may be coupled with one or more processors 102, 202 and may store various types of data, signals, messages, information, programs, codes, instructions and/or instructions. One or more memories 104, 204 may be comprised of ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof. One or more memories 104, 204 may be located internally and/or external to one or more processors 102, 202. Additionally, one or more memories 104, 204 may be coupled to one or more processors 102, 202 through various technologies, such as wired or wireless connections.

One or more transceivers 106, 206 may transmit user data, control information, radio signals/channels, etc., as referred to in the methods and/or operational flow charts herein, to one or more other devices. One or more transceivers 106, 206 may receive user data, control information, radio signals/channels, etc. referred to in descriptions, functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. there is. For example, one or more transceivers 106 and 206 may be connected to one or more processors 102 and 202 and transmit and receive wireless signals. For example, one or more processors 102, 202 may control one or more transceivers 106, 206 to transmit user data, control information, or radio signals to one or more other devices. Additionally, one or more processors 102, 202 may control one or more transceivers 106, 206 to receive user data, control information, or radio signals from one or more other devices. In addition, one or more transceivers 106, 206 may be coupled with one or more antennas 108, 208, and one or more transceivers 106, 206 via one or more antennas 108, 208, as described herein, function. , procedures, proposals, methods and / or operation flowcharts, etc. can be set to transmit and receive user data, control information, radio signals / channels, etc. In this document, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports). One or more transceivers (106, 206) convert the received radio signals/channels from RF band signals in order to process the received user data, control information, radio signals/channels, etc. using one or more processors (102, 202). It can be converted into a baseband signal. One or more transceivers 106 and 206 may convert user data, control information, and radio signals/channels processed by one or more processors 102 and 202 from baseband signals to RF band signals. To this end, one or more of the transceivers 106, 206 may include (analog) oscillators and/or filters.

45 illustrates another example of a wireless device applicable to the present specification.

45, a wireless device may include at least one processor 102, 202, at least one memory 104, 204, at least one transceiver 106, 206, and one or more antennas 108, 208. there is.

As a difference between the example of the wireless device described above in FIG. 44 and the example of the wireless device in FIG. 45, in FIG. 44, the processors 102 and 202 and the memories 104 and 204 are separated, but in the example of FIG. 45, the processor Note that (102, 202) includes the memory (104, 204).

Here, since the detailed description of the processors 102 and 202, the memories 104 and 204, the transceivers 106 and 206, and one or more antennas 108 and 208 have been described above, in order to avoid repetition of unnecessary description, Description of repeated description is omitted.

Hereinafter, an example of a signal processing circuit to which the present specification is applied will be described.

46 illustrates a signal processing circuit for a transmission signal.

Referring to FIG. 46 , the signal processing circuit 1000 may include a scrambler 1010, a modulator 1020, a layer mapper 1030, a precoder 1040, a resource mapper 1050, and a signal generator 1060. there is. Although not limited to this, the operations/functions of FIG. 46 may be performed by processors 102 and 202 and/or transceivers 106 and 206 of FIG. 44 . The hardware elements of FIG. 46 may be implemented in processors 102 and 202 and/or transceivers 106 and 206 of FIG. 44 . For example, blocks 1010-1060 may be implemented in the processors 102 and 202 of FIG. 44 . Also, blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 44 , and block 1060 may be implemented in the transceivers 106 and 206 of FIG. 44 .

The codeword may be converted into a radio signal through the signal processing circuit 1000 of FIG. 46 . Here, a codeword is an encoded bit sequence of an information block. Information blocks may include transport blocks (eg, UL-SCH transport blocks, DL-SCH transport blocks). Radio signals may be transmitted through various physical channels (eg, PUSCH, PDSCH).

Specifically, the codeword may be converted into a scrambled bit sequence by the scrambler 1010. A scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device. The scrambled bit sequence may be modulated into a modulation symbol sequence by modulator 1020. The modulation scheme may include pi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying (m-PSK), m-Quadrature Amplitude Modulation (m-QAM), and the like. The complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 1030. Modulation symbols of each transport layer may be mapped to the corresponding antenna port(s) by the precoder 1040 (precoding). The output z of the precoder 1040 can be obtained by multiplying the output y of the layer mapper 1030 by the N*M precoding matrix W. Here, N is the number of antenna ports and M is the number of transport layers. Here, the precoder 1040 may perform precoding after performing transform precoding (eg, DFT transformation) on complex modulation symbols. Also, the precoder 1040 may perform precoding without performing transform precoding.

The resource mapper 1050 may map modulation symbols of each antenna port to time-frequency resources. The time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain. The signal generator 1060 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna. To this end, the signal generator 1060 may include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like. .

The signal processing process for the received signal in the wireless device may be configured in reverse to the signal processing process 1010 to 1060 of FIG. 46 . For example, wireless devices (eg, 100 and 200 of FIG. 44 ) may receive a wireless signal from the outside through an antenna port/transceiver. The received radio signal may be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scramble process. The codeword may be restored to an original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

Hereinafter, an example of using a wireless device to which the present specification is applied will be described.

47 shows another example of a wireless device applied to this specification. A wireless device may be implemented in various forms according to usage-examples/services (see FIG. 43).

Referring to FIG. 47, wireless devices 100 and 200 correspond to the wireless devices 100 and 200 of FIG. 44, and include various elements, components, units/units, and/or modules. ) can be configured. For example, the wireless devices 100 and 200 may include a communication unit 110 , a control unit 120 , a memory unit 130 and an additional element 140 . The communication unit may include communication circuitry 112 and transceiver(s) 114 . For example, communication circuitry 112 may include one or more processors 102, 202 of FIG. 44 and/or one or more memories 104, 204. For example, transceiver(s) 114 may include one or more transceivers 106, 206 of FIG. 44 and/or one or more antennas 108, 208. The control unit 120 is electrically connected to the communication unit 110, the memory unit 130, and the additional element 140 and controls overall operations of the wireless device. For example, the control unit 120 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 130. In addition, the controller 120 transmits the information stored in the memory unit 130 to the outside (eg, another communication device) through the communication unit 110 through a wireless/wired interface, or transmits the information stored in the memory unit 130 to the outside (eg, another communication device) through the communication unit 110. Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 130 .

The additional element 140 may be configured in various ways according to the type of wireless device. For example, the additional element 140 may include at least one of a power unit/battery, an I/O unit, a driving unit, and a computing unit. Although not limited thereto, the wireless device may be a robot (Fig. 43, 100a), a vehicle (Fig. 43, 100b-1, 100b-2), an XR device (Fig. 43, 100c), a mobile device (Fig. 43, 100d), a home appliance. (FIG. 43, 100e), IoT device (FIG. 43, 100f), digital broadcasting terminal, hologram device, public safety device, MTC device, medical device, fintech device (or financial device), security device, climate/environmental device, It may be implemented in the form of an AI server/device (Fig. 43, 400), a base station (Fig. 43, 200), a network node, or the like. Wireless devices can be mobile or used in a fixed location depending on the use-case/service.

In FIG. 47, various elements, components, units/units, and/or modules in the wireless devices 100 and 200 may be entirely interconnected through a wired interface or at least partially connected wirelessly through the communication unit 110. For example, in the wireless devices 100 and 200, the control unit 120 and the communication unit 110 are connected by wire, and the control unit 120 and the first units (eg, 130 and 140) are connected through the communication unit 110. Can be connected wirelessly. Additionally, each element, component, unit/unit, and/or module within the wireless device 100, 200 may further include one or more elements. For example, the control unit 120 may be composed of one or more processor sets. For example, the controller 120 may include a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like. As another example, the memory unit 130 may include random access memory (RAM), dynamic RAM (DRAM), read only memory (ROM), flash memory, volatile memory, and non-volatile memory. volatile memory) and/or a combination thereof.

Hereinafter, an implementation example of FIG. 47 will be described in more detail with reference to drawings.

48 illustrates a portable device applied to this specification. A portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), and a portable computer (eg, a laptop computer). A mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

Referring to FIG. 48, the portable device 100 includes an antenna unit 108, a communication unit 110, a control unit 120, a memory unit 130, a power supply unit 140a, an interface unit 140b, and an input/output unit 140c. ) may be included. The antenna unit 108 may be configured as part of the communication unit 110 . Blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 47 .

The communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The controller 120 may perform various operations by controlling components of the portable device 100 . The control unit 120 may include an application processor (AP). The memory unit 130 may store data/parameters/programs/codes/commands necessary for driving the portable device 100 . In addition, the memory unit 130 may store input/output data/information. The power supply unit 140a supplies power to the portable device 100 and may include a wired/wireless charging circuit, a battery, and the like. The interface unit 140b may support connection between the portable device 100 and other external devices. The interface unit 140b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices. The input/output unit 140c may receive or output image information/signal, audio information/signal, data, and/or information input from a user. The input/output unit 140c may include a camera, a microphone, a user input unit, a display unit 140d, a speaker, and/or a haptic module.

For example, in the case of data communication, the input/output unit 140c obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the acquired information/signals are stored in the memory unit 130. can be stored The communication unit 110 may convert the information/signal stored in the memory into a wireless signal, and directly transmit the converted wireless signal to another wireless device or to a base station. In addition, the communication unit 110 may receive a radio signal from another wireless device or a base station and then restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 130, it may be output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 140c.

49 illustrates a vehicle or autonomous vehicle applied in this specification.

Vehicles or autonomous vehicles may be implemented as mobile robots, vehicles, trains, manned/unmanned aerial vehicles (AVs), ships, and the like.

Referring to FIG. 49, a vehicle or autonomous vehicle 100 includes an antenna unit 108, a communication unit 110, a control unit 120, a driving unit 140a, a power supply unit 140b, a sensor unit 140c, and an autonomous driving unit. A portion 140d may be included. The antenna unit 108 may be configured as part of the communication unit 110 . Blocks 110/130/140a to 140d respectively correspond to blocks 110/130/140 of FIG. 47 .

The communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (e.g. base stations, roadside base stations, etc.), servers, and the like. The controller 120 may perform various operations by controlling elements of the vehicle or autonomous vehicle 100 . The controller 120 may include an Electronic Control Unit (ECU). The driving unit 140a may drive the vehicle or autonomous vehicle 100 on the ground. The driving unit 140a may include an engine, a motor, a power train, a wheel, a brake, a steering device, and the like. The power supply unit 140b supplies power to the vehicle or autonomous vehicle 100, and may include a wired/wireless charging circuit, a battery, and the like. The sensor unit 140c may obtain vehicle conditions, surrounding environment information, and user information. The sensor unit 140c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, and a vehicle forward. /Can include a reverse sensor, battery sensor, fuel sensor, tire sensor, steering sensor, temperature sensor, humidity sensor, ultrasonic sensor, illuminance sensor, pedal position sensor, and the like. The autonomous driving unit 140d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set and driving. technology can be implemented.

For example, the communication unit 110 may receive map data, traffic information data, and the like from an external server. The autonomous driving unit 140d may generate an autonomous driving route and a driving plan based on the acquired data. The controller 120 may control the driving unit 140a so that the vehicle or autonomous vehicle 100 moves along the autonomous driving path according to the driving plan (eg, speed/direction adjustment). During autonomous driving, the communicator 110 may non-/periodically obtain the latest traffic information data from an external server and obtain surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit 140c may acquire vehicle state and surrounding environment information. The autonomous driving unit 140d may update an autonomous driving route and a driving plan based on newly acquired data/information. The communication unit 110 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server. The external server may predict traffic information data in advance using AI technology based on information collected from the vehicle or self-driving vehicles, and may provide the predicted traffic information data to the vehicle or self-driving vehicles.

50 illustrates a vehicle applied to this specification. A vehicle may be implemented as a means of transportation, a train, an air vehicle, a ship, and the like.

Referring to FIG. 50 , the vehicle 100 may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, and a position measurement unit 140b. Here, blocks 110 to 130/140a to 140b correspond to blocks 110 to 130/140 of FIG. X3, respectively.

The communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) with other vehicles or external devices such as base stations. The controller 120 may perform various operations by controlling components of the vehicle 100 . The memory unit 130 may store data/parameters/programs/codes/commands supporting various functions of the vehicle 100 . The input/output unit 140a may output an AR/VR object based on information in the memory unit 130. The input/output unit 140a may include a HUD. The location measurement unit 140b may obtain location information of the vehicle 100 . The location information may include absolute location information of the vehicle 100, location information within a driving line, acceleration information, and location information with neighboring vehicles. The location measurement unit 140b may include GPS and various sensors.

For example, the communication unit 110 of the vehicle 100 may receive map information, traffic information, and the like from an external server and store them in the memory unit 130 . The location measurement unit 140b may acquire vehicle location information through GPS and various sensors and store it in the memory unit 130 . The controller 120 may generate a virtual object based on map information, traffic information, vehicle location information, etc., and the input/output unit 140a may display the created virtual object on a window in the vehicle (1410, 1420). In addition, the controller 120 may determine whether the vehicle 100 is normally operated within the driving line based on the vehicle location information. When the vehicle 100 abnormally deviate from the driving line, the controller 120 may display a warning on a window in the vehicle through the input/output unit 140a. In addition, the controller 120 may broadcast a warning message about driving abnormality to surrounding vehicles through the communication unit 110 . Depending on circumstances, the controller 120 may transmit vehicle location information and information on driving/vehicle abnormalities to related agencies through the communication unit 110 .

51 illustrates an XR instrument applied herein. The XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.

Referring to FIG. 51 , the XR device 100a may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, a sensor unit 140b, and a power supply unit 140c. . Here, blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. X3, respectively.

The communication unit 110 may transmit/receive signals (eg, media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. Media data may include video, image, sound, and the like. The controller 120 may perform various operations by controlling components of the XR device 100a. For example, the controller 120 may be configured to control and/or perform procedures such as video/image acquisition, (video/image) encoding, and metadata generation and processing. The memory unit 130 may store data/parameters/programs/codes/commands necessary for driving the XR device 100a/creating an XR object. The input/output unit 140a may obtain control information, data, etc. from the outside and output the created XR object. The input/output unit 140a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 140b may obtain XR device status, surrounding environment information, user information, and the like. The sensor unit 140b may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is. The power supply unit 140c supplies power to the XR device 100a and may include a wired/wireless charging circuit, a battery, and the like.

For example, the memory unit 130 of the XR device 100a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object). The input/output unit 140a may obtain a command to operate the XR device 100a from a user, and the control unit 120 may drive the XR device 100a according to the user's driving command. For example, when a user tries to watch a movie, news, etc. through the XR device 100a, the control unit 120 transmits content request information to another device (eg, the mobile device 100b) or through the communication unit 130. can be sent to the media server. The communication unit 130 may download/stream content such as movies and news from another device (eg, the portable device 100b) or a media server to the memory unit 130 . The control unit 120 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, metadata generation/processing, etc. for content, and acquisition through the input/output unit 140a/sensor unit 140b. An XR object may be created/output based on information about a surrounding space or a real object.

In addition, the XR device 100a is wirelessly connected to the portable device 100b through the communication unit 110, and the operation of the XR device 100a may be controlled by the portable device 100b. For example, the mobile device 100b may operate as a controller for the XR device 100a. To this end, the XR device 100a may acquire 3D location information of the portable device 100b and then generate and output an XR object corresponding to the portable device 100b.

52 illustrates a robot applied in this specification. Robots may be classified into industrial, medical, household, military, and the like depending on the purpose or field of use.

Referring to FIG. 52 , the robot 100 may include a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a, a sensor unit 140b, and a driving unit 140c. Here, blocks 110 to 130/140a to 140c correspond to blocks 110 to 130/140 of FIG. X3, respectively.

The communication unit 110 may transmit/receive signals (eg, driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The controller 120 may perform various operations by controlling components of the robot 100 . The memory unit 130 may store data/parameters/programs/codes/commands supporting various functions of the robot 100. The input/output unit 140a may obtain information from the outside of the robot 100 and output the information to the outside of the robot 100 . The input/output unit 140a may include a camera, a microphone, a user input unit, a display unit, a speaker, and/or a haptic module. The sensor unit 140b may obtain internal information of the robot 100, surrounding environment information, user information, and the like. The sensor unit 140b may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a radar, and the like. The driving unit 140c may perform various physical operations such as moving a robot joint. In addition, the driving unit 140c may make the robot 100 drive on the ground or fly in the air. The driving unit 140c may include actuators, motors, wheels, brakes, propellers, and the like.

53 illustrates an AI device applied to this specification.

AI devices include fixed or mobile devices such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, and vehicles. It can be implemented with possible devices and the like.

Referring to FIG. 53, the AI device 100 includes a communication unit 110, a control unit 120, a memory unit 130, an input/output unit 140a/140b, a running processor unit 140c, and a sensor unit 140d. can include Blocks 110-130/140a-140d correspond to blocks 110-130/140 of Figure X3, respectively.

The communication unit 110 transmits wired/wireless signals (eg, sensor information, user input, learning) to other AI devices (eg, FIG. W1, 100x, 200, 400) or external devices such as the AI server 200 using wired/wireless communication technology. models, control signals, etc.) can be transmitted and received. To this end, the communication unit 110 may transmit information in the memory unit 130 to an external device or transmit a signal received from the external device to the memory unit 130 .

The controller 120 may determine at least one feasible operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the controller 120 may perform the determined operation by controlling components of the AI device 100 . For example, the controller 120 may request, retrieve, receive, or utilize data from the learning processor unit 140c or the memory unit 130, and may perform a predicted operation among at least one feasible operation or an operation determined to be desirable. Components of the AI device 100 may be controlled to execute an operation. In addition, the control unit 120 collects history information including user feedback on the operation contents or operation of the AI device 100 and stores it in the memory unit 130 or the running processor unit 140c, or the AI server ( It can be transmitted to an external device such as FIG. W1, 400). The collected history information can be used to update the learning model.

The memory unit 130 may store data supporting various functions of the AI device 100 . For example, the memory unit 130 may store data obtained from the input unit 140a, data obtained from the communication unit 110, output data from the learning processor unit 140c, and data obtained from the sensing unit 140. In addition, the memory unit 130 may store control information and/or software codes necessary for operation/execution of the control unit 120 .

The input unit 140a may obtain various types of data from the outside of the AI device 100. For example, the input unit 120 may obtain learning data for model learning and input data to which the learning model is to be applied. The input unit 140a may include a camera, a microphone, and/or a user input unit. The output unit 140b may generate an output related to sight, hearing, or touch. The output unit 140b may include a display unit, a speaker, and/or a haptic module. The sensing unit 140 may obtain at least one of internal information of the AI device 100, surrounding environment information of the AI device 100, and user information by using various sensors. The sensing unit 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.

The learning processor unit 140c may learn a model composed of an artificial neural network using learning data. The running processor unit 140c may perform AI processing together with the running processor unit of the AI server (FIG. W1, 400). The learning processor unit 140c may process information received from an external device through the communication unit 110 and/or information stored in the memory unit 130 . In addition, the output value of the learning processor unit 140c may be transmitted to an external device through the communication unit 110 and/or stored in the memory unit 130.

The claims set forth herein can be combined in a variety of ways. For example, the technical features of the method claims of this specification may be combined to be implemented as a device, and the technical features of the device claims of this specification may be combined to be implemented as a method. In addition, the technical features of the method claims of the present specification and the technical features of the device claims may be combined to be implemented as a device, and the technical features of the method claims of the present specification and the technical features of the device claims may be combined to be implemented as a method.

Claims (15)

In a method for estimating an error rate, performed by an apparatus in a quantum cryptography communication system,
Receive a random access (RA) preamble from another device;
Transmitting a random access response (RAR) to the other device in response to the RA preamble;
performing a radio resource control (RRC) connection procedure with the other device;
receive data from the other device; and
Decrypt the data based on the key information,
the key information is determined based on the estimation of the error rate for the selected key information received from the other device;
The device,
receive first syndrome information from the other device;
derive second syndrome information based on the selection key information and the parity check matrix;
detect odd-numbered errors and even-numbered errors related to the screening key information, based on the first syndrome information and the second syndrome information;
performing an estimate of the error rate based on the odd number of errors and the even number of errors;
Based on, estimating the error rate. The method of claim 1, wherein the device receives the first syndrome information through a public channel between the device and the other device. The method of claim 1, wherein the device receives the screening key information through a quantum channel between the device and the other device. The method of claim 1, wherein the selection key information includes a plurality of bits,
wherein the plurality of bits are grouped into a plurality of bit groups. 5. The method of claim 4, wherein each of the first syndrome information and the second syndrome information is determined in units of the bit group. The method of claim 1, wherein the apparatus detects the odd number of errors through the first syndrome information and the second syndrome information based on a single bit syndrome comparison. 7. The method of claim 6, wherein the apparatus detects the even number of errors when the odd number of errors is not detected. The method of claim 7 , wherein the apparatus detects the even number of errors through the first syndrome information and the second syndrome information based on M-bit syndrome comparison,
Wherein M is a positive integer. The method of claim 8, wherein the device determines that there is no error in the selection key information when the even number of errors is not detected. In a method for estimating an error rate for key information, performed by an apparatus in a quantum cryptography communication system,
receive first syndrome information from the other device;
derive second syndrome information based on the selection key information and the parity check matrix;
detect odd-numbered errors and even-numbered errors related to the screening key information, based on the first syndrome information and the second syndrome information;
and performing the estimation of the error rate based on the odd number of errors and the even number of errors. device,
transceiver;
at least one memory; and
at least one processor operably coupled with the at least one memory and the transceiver, the processor comprising:
configured to control the transceiver to receive a random access (RA) preamble from another device;
configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble;
configured to perform a radio resource control (RRC) connection procedure with the other device;
configured to control the transceiver to receive data from the other device; and
configured to decrypt the data based on key information;
the key information is determined based on an estimation of an error rate for the selected key information received from the other device;
The device,
receive first syndrome information from the other device;
derive second syndrome information based on the selection key information and the parity check matrix;
detect odd-numbered errors and even-numbered errors related to the screening key information, based on the first syndrome information and the second syndrome information;
performing an estimate of the error rate based on the odd number of errors and the even number of errors;
Apparatus characterized in that for estimating the error rate based on. device,
at least one memory; and
at least one processor operably coupled to the at least one memory, the processor comprising:
configured to control the transceiver to receive a random access (RA) preamble from another device;
configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble;
configured to perform a radio resource control (RRC) connection procedure with the other device;
configured to control the transceiver to receive data from the other device; and
configured to decrypt the data based on key information;
the key information is determined based on an estimation of an error rate for the selected key information received from the other device;
The device,
receive first syndrome information from the other device;
derive second syndrome information based on the selection key information and the parity check matrix;
detect odd-numbered errors and even-numbered errors related to the screening key information, based on the first syndrome information and the second syndrome information;
performing an estimate of the error rate based on the odd number of errors and the even number of errors;
Apparatus characterized in that for estimating the error rate based on. In at least one computer readable medium containing instructions based on being executed by at least one processor,
configured to control the transceiver to receive a random access (RA) preamble from another device;
configured to control the transceiver to transmit a random access response (RAR) to the other device in response to the RA preamble;
configured to perform a radio resource control (RRC) connection procedure with the other device;
configured to control the transceiver to receive data from the other device; and
configured to decrypt the data based on key information;
the key information is determined based on an estimation of an error rate for the selected key information received from the other device;
The device,
receive first syndrome information from the other device;
derive second syndrome information based on the selection key information and the parity check matrix;
detect odd-numbered errors and even-numbered errors related to the screening key information, based on the first syndrome information and the second syndrome information;
performing an estimate of the error rate based on the odd number of errors and the even number of errors;
Based on , the error rate is estimated. A method for transmitting first syndrome information, performed by an apparatus in a quantum cryptography communication system, comprising:
transmits a random access (RA) preamble to another device;
Receiving a random access response (RAR) from the other device in response to the RA preamble;
performing a radio resource control (RRC) connection procedure with the other device; and
Transmitting data to the other device,
The device encodes the data based on key information,
the device transmits the first syndrome information related to the key information to the other device;
The method of claim 1 , wherein the device transmits selection key information related to the key information to the other device. device,
transceiver;
at least one memory; and
at least one processor operably coupled with the at least one memory and the transceiver, the processor comprising:
configured to control the transceiver to transmit a random access (RA) preamble to another device;
configured to control the transceiver to receive a random access response (RAR) from the other device in response to the RA preamble;
configured to perform a radio resource control (RRC) connection procedure with the other device; and
Is configured to control the transceiver to transmit data to the other device,
The device encodes the data based on key information,
The device transmits first syndrome information related to the key information to the other device,
wherein the device transmits selection key information related to the key information to the other device.

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