KR20230031196A - Plug-and-play quantum key distribution method based on multipath and wavelength division and apparatus using the method - Google Patents

Abstract

In the present specification, in a method for communicating with another device through a quantum channel, performed by a device, n first pulse trains each having a different wavelength are generated, wherein n is a natural number, and Based on the length and the length of the storage line, k second pulse trains having different wavelengths are selected from among the first pulse trains, where k is a natural number and k is equal to or smaller than n; and transmitting the k second pulse trains to the other device through the quantum channel based on the k multipaths, wherein each of the k multipaths is composed of delay lines having different lengths. provides a way to

Description

Plug-and-play quantum key distribution method based on multipath and wavelength division and apparatus using the method

This specification relates to quantum communication.

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.

On the other hand, since the existing quantum cryptography communication technique is only applied to some communication networks due to some bulky elements, there is a limit to expanding the application area of the quantum cryptography communication technique.

Accordingly, the present specification intends to provide configurations capable of reducing the volume of the Bob side and/or Alice side in quantum communication.

In addition, in the existing plug-and-play quantum cryptography communication technique, one pulse train reciprocates between the transmitter and receiver to prevent an increase in error due to Rayleigh back scattering, and the next pulse train cannot be transmitted until it is detected, resulting in a low key rate.

Therefore, in this specification, a method for improving the transmission speed of quantum keys in quantum cryptographic communication is proposed.

According to an embodiment of the present specification, n first pulse trains each having a different wavelength are generated, and among the n first pulse trains, based on the length of the quantum channel and the length of the storage line, Selecting k second pulse trains having different wavelengths and transmitting the k second pulse trains to the other device through the quantum channel based on the k multipaths; Each of is provided with a method and a device using the same, characterized in that composed of delay lines of different lengths.

According to the present specification, two effects can be expected through a technique of applying wavelength division and time division. First, in quantum communication, the size of the Bob side and/or Alice side can be reduced. Second, the low quantum key transmission speed of the two-way quantum cryptography communication technique can be improved.

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 schematically illustrates an example of quantum cryptographic communication.
5 schematically illustrates an example of a plug and play QKD protocol.
6 is an example of a time delay between pulse trains of the plug and play QKD technique.
7 is a flowchart of a quantum cryptography communication method based on wavelength division and multipath according to an embodiment of the present specification.
8 schematically illustrates a plug and play QKD protocol to be provided according to an embodiment of the present specification.
9 schematically illustrates changes in a wavelength division, a time division structure, and a pulse train applied to a bob side according to an embodiment of the present specification.
10 schematically illustrates an example of setting delay line lengths of k multipaths.
11 schematically illustrates an application example of an optical filter for removing back scattering pulses.
12 schematically illustrates an example of a configuration of a detection unit to which two MUXs and 2k SPDs are applied.
FIG. 13 schematically illustrates changes in a wavelength division, time division structure, and pulse train applied to a bob side according to another embodiment of the present specification.
FIG. 14 schematically shows the difference between methods of detecting key information of the same length in the first technique and the second technique.
15 is a flowchart of a quantum cryptography communication method based on wavelength division and multipath by a device in a bob side according to an embodiment of the present specification.
16 schematically illustrates a block diagram of a quantum cryptography communication device based on wavelength splitting and multipath by a device in a bob side according to an embodiment of the present specification.
FIG. 17 schematically illustrates a block diagram of a quantum cryptography communication device based on wavelength splitting and multipath by a device in a bob side according to another embodiment of the present specification.
18 illustrates a communication system 1 applied to this specification.
19 illustrates a wireless device applicable to this specification.
20 shows another example of a wireless device applicable to the present specification.
21 illustrates a signal processing circuit for a transmission signal.
22 shows another example of a wireless device applied to the present specification.
23 illustrates a portable device applied to this specification.
24 illustrates a vehicle or autonomous vehicle applied to this specification.
25 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.
26 illustrates an XR device applied herein.
27 illustrates a robot applied in this specification.
28 illustrates an AI device applied to this specification.
29 is a diagram showing an example of a communication structure that can be provided in a 6G system.
30 schematically illustrates an example of a perceptron structure.
31 schematically illustrates an example of a multilayer perceptron structure.
32 schematically illustrates a deep neural network example.
33 schematically illustrates an example of a convolutional neural network.
34 schematically illustrates an example of a filter operation in a convolutional neural network.
35 schematically illustrates an example of a neural network structure in which a cyclic loop exists.
36 schematically illustrates an example of an operating structure of a recurrent neural network.
37 shows an example of an electromagnetic spectrum.
38 is a diagram showing an example of THz communication application.
39 is a diagram illustrating an example of an electronic element-based THz wireless communication transceiver.
40 is a diagram illustrating an example of a method for generating a THz signal based on an optical device, and FIG. 41 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical device.
42 illustrates a structure of a transmitter based on a photoinc source, and FIG. 43 illustrates a structure of an optical modulator.

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” herein means “only A”, “only B”, “only C”, or “any combination of A, B and C ( any combination of A, B and C)”.

A slash (/) or comma (comma) used in this specification may mean “and/or”. For example, “A/B” may mean “A and/or B”. Accordingly, "A/B" can 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”. In addition, 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 of A and B (at least one of A and B)”.

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 it is 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 can also be applied to other 5G usage scenarios not shown in FIG. 8 .

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.

Meanwhile, the NR communication system and the LTE communication system described above may also be applied to quantum cryptography communication, which will be described later.

<Quantum cryptographic communication>

On the other hand, due to the appearance of quantum computers, it has become possible to hack existing cryptographic systems based on mathematical complexity (eg, RSA, AES, etc.). In order to prevent hacking, quantum cryptographic communication has been proposed, and an example of a quantum cryptographic communication structure may be described through the drawings as follows.

4 schematically illustrates an example of quantum cryptographic communication.

According to FIG. 4 , a quantum key distribution (QKD) transmitter 410 may perform communication by being connected to a QKD receiver 420 through a public channel and a quantum channel.

At this time, the QKD transmitter 410 may supply the secret key to the encrypter 430, and the QKD receiver 420 may also supply the secret key to the decryptor 440. Here, plain text may be input/output to the encryptor 430, and the encrypter 430 may transmit data encrypted with a secret symmetric key to the decryptor 440 (through an existing communication network). . In addition, plain text may be input/output to the decoder 440 .

According to quantum cryptographic communication, since a secret key for data encryption is distributed using quantum mechanics, it may be impossible for an eavesdropper to find out the information of the encryption key. In summary, quantum cryptographic communication may have the following properties.

- Impossibility of copying quantum information: It is impossible to copy quantum information passing through a quantum communication channel

- Irreversibility of quantum measurement (the state in which a single photon is measured once may not be in its original state): intercept and resend attack impossible

In addition, in quantum cryptographic communication, it is possible to check eavesdropping by checking a quantum bit error rate every time a key is transmitted.

On the other hand, Plug and Play QKD is a protocol that generates key information through phase encoding, and is highly resistant to fluctuations in phase and polarization, so it is an efficient quantum cryptography protocol that does not require the application of additional correction techniques. .

In addition, this technique is not a symmetrical structure in which the Alice side and the Bob side each have a light source and a detector, as shown in the configuration diagram of FIG. 5 below, but a light source and a detector on the Bob side. It can have an asymmetric structure that everyone has.

Due to this structural feature, only a few optical elements required for encoding need to exist on the Alice side, so the quantum communication system is a low-complexity one-to-many quantum cryptographic communication network configuration having one Bob side and multiple Alice sides. and can be configured at low cost. Hereinafter, a plug and play QKD protocol among various protocols of QKD technology will be described through drawings.

5 schematically illustrates an example of a plug and play QKD protocol.

According to FIG. 5, the basic plug and play QKD technique may proceed in the following order.

1) The strong laser pulse (1550nm) emitted from the Bob side is split 50/50 by BS (Beam splitter). Expressing this as an expression, it can be as follows.

[Equation 1]

2) Two pulses passing through the BS are divided into a short path and a long path with a phase modulator (eg, PM_B) and a delay line (DL). At this time, the phase modulator PM_B does not operate. And DL is used to create a time difference so that the pulses passing through the two paths do not overlap at the same time, and generally has a length of several tens of meters. Expressing this as an expression, it can be as follows.

[Equation 2]

3) Pulses passing through the short path and the long path have polarization components perpendicular to each other when passing through the PBS (Polarized Beam Splitter). Therefore, a pulse passing through the short path has the same polarization component as the input pulse even after passing through the PBS, but a pulse passing through the long path has a polarization perpendicular to the input pulse after passing through the PBS. Expressing this as an expression, it can be as follows.

[Equation 3]

4) The two pulses passing through the PBS pass through the quantum channel with a time difference and move towards Alice.

5) Pulses coming through the channel pass through an attenuator and a phase modulator (eg, PM_A), but both do not operate at this time.

6) A pulse passing through PM_A passes through a storage line (SL). At this time, the role of the SL (Storage line) is as follows. SL prevents mixing of pulses detected by the detector and pulses including key information measured by the detector due to Rayleigh back-scattering.

In addition, it plays a role in distinguishing between the back scattering pulse generated from the pulse coming from Bob to Alice in PM_A and the pulse returning from Alice after passing FM.

Back scattering occurs when a pulse generated from a laser is partially reflected as it passes through each element, and non-key information is measured by a detector. Many errors may occur due to backscattering, which is a factor that significantly degrades the performance of quantum cryptography communication.

In the plug-and-play QKD protocol, when the efficiency of the single photon detector is n_d and the pulse width is δ, the effect of Rayleigh back scattering is the number of photons P_in generated per second on the bob side and the number of double Rayleigh back scattering occurring. By the relationship of P_rayleigh, it can be expressed as the following equation.

[Equation 4]

Here, P_in is defined as P_in=f_laser n_b (f_laser: pulse repetition rate, n_b: total loss factor on the bob side), α is 0.21 dB/km, which is a fiber loss of 1550 nm, L is the length of the fiber between Alice and Bob, and β is the Rayleigh back scattering coefficient. Therefore, it can be seen that the larger P_in is, the larger the influence of Rayleigh-back scattering is. Therefore, to solve this problem, a storage line with a length long enough to store the pulse train generated by the bob side may be required.

For example, assuming that pulses are generated at 1MHz (e.g. 10^(-6)(s)), the transmission speed of light is 2×10^8(m/s) in a wired optical fiber, and the number of keys generated at once is 125. Then, a total of 2×(10^8(m/s))×10^(-6)(s)×125=25 km of storage lines may be required. In this case, since the storage line SL may store photons while reciprocating, the actual length may be 12.5 km or more, which is half of 25 km. As such, in the current plug-and-play QKD technique, a storage line with a very long length is applied to the Alice side. Therefore, in order to apply this technology to various areas such as one-to-many quantum cryptography technology in the future, it is necessary to reduce the weight of the Alice side by minimizing the length of the SL. may be needed

7) In the FM (Faraday Mirror), the polarization of the pulse emitted from the rice side passing through the PBS is reflected and then changed 90 degrees again. (The role of the Faraday Mirror: It changes the incident polarization to polarization perpendicular to it, so that when the pulse goes back to Bob, the short path pulse enters the long path by PBS, and the long path pulse goes short path Therefore, since the polarization is perpendicular to each other when traveling and when reflecting back, the birefringence experienced by light pulses on the optical fiber cancel each other, making it possible to build a stable system.) This can be expressed as the following equation.

[Equation 5]

8) A pulse that passes through SL after being reflected from M generates key information through phase coding in a phase modulator (eg, PM_A). Key information according to the phase is shown in Table 1 below.

phase key value 0 (-) 0 π/2 (\) 0 π (｜) One 3π/2 (/) One

As described above, phase coding may be performed by applying four different phases (θ_A) to the second pulse passing through PM_A on the Alice side. Expressing this as an expression, it can be as follows.

[Equation 6]

9) After reducing the intensity of the optical signal to the level of a single photon using an attenuator, a pulse is transmitted through a quantum channel. (At this time, the 0.1 photon level is generally used.)

10) The polarization state of the pulse when exiting the PBS to the quantum channel and the pulse when entering the PBS from the quantum channel are opposite due to FM. Therefore, the two pulses that go through the long path and the short path when going out from Bob to the channel will go through the opposite path when returning from the channel to Bob. Expressing this as an expression, it can be as follows.

[Equation 7]

11) The first pulse entering Bob through the quantum channel passes through a long path and determines the measurement basis by applying two phases (θ_B) as shown in Table 2 in PM_B.

phase basis value 0 + π/2 X

Expressing this as an expression, it can be as follows.

[Equation 8]

12) The first pulse (passing the long path) and the second pulse (short path) passing through the two paths of the bob side arrive at the BS at the same time, and at this time, they overlap each other and cause constructive or destructive interference. Expressing this as an expression, it can be as follows.

[Equation 9]

13) The detection result according to the overlapping result is determined which of detectors 1 and 2 will be measured, and the result may be shown in Table 3 below.

Bob/Alice 0 (0, -) π/2(0, \) π(1, ｜) 3π/2(1, /) 0, + One 2 π/2, X One 2

Meanwhile, the storage line described above can be applied for the following reason (ie, to solve the problem of backscattering photons): - Light passing through an optical fiber is scattered.

- At this time, a small amount of light can be re-captured by the fiber in the reverse direction.

- Pulses from bob may be brighter than pulses from alice and may contain less than 0.1 photons on average.

- Backscattering photons can have pulses propagating back to Bob, which can cause false counts.

Meanwhile, a storage line (SL) may have a generally long length as described below, and because of this, it is not easy to miniaturize an Alice side (eg, a device corresponding to the Alice side).

- Role of SL: Solving the problem of increasing QBER due to Rayleigh backscattering.

- Requirements for minimum length of SL: In order to prevent back scattering, the length must be such that all laser pulse trains can be stored.

[Equation 10]

(number of keys * speed of optical pulse in fiber * (1/f_laser))/2

Here, for example, when 256 keys are generated and f_laser is 100 MHz, the storage line may have a length of, for example, 256 m or more. As can be seen from this example, miniaturization/lightening of storage lines is generally a difficult task.

Hereinafter, this specification will be described in more detail.

Existing quantum cryptography communication techniques are applied only to wired and wireless communication networks due to some bulky devices, so there is a limit to expanding the application area of quantum cryptography communication techniques. In addition, as a requirement for quantum to the home, there is a demand to miniaturize the laser and the detector respectively present in the transceiver, but this has difficulties in device characteristics and structure.

On the other hand, in the plug-and-play QKD, the laser and the detector have a structural feature of being together in the receiver (eg, bob side) (however, in this specification, the laser and the detector are present on the Alice side, for example, or the laser and the detector are on the bob side and It is not intended to exclude the configurations that each exist on the Alice side from the scope of rights). Because of this, the plug and play QKD has a structure that is advantageous for lightening the transmission unit (eg, Alice side), and because of this, the plug and play QKD may be advantageous to apply to a structure such as a 1:N QKD network and / or IOT.

In the above circumstances, the present specification intends to propose a multi-wavelength splitting technique of a multi-wavelength laser source in order to provide a method of minimizing the length of a storage line, which has been difficult to reduce the weight of the conventional Alice side. Through this, the length of the storage line of the Alice side can be minimized, and thus, the weight of the Alice side can be provided.

On the other hand, in the case of plug-and-play QKD, compared to one-way QKD, the key rate is low due to the structural characteristics of the transceiver having to go back and forth (constraints to minimize wrong detection due to back scattering: The next train pulse(s) can only be sent after the previously sent key train pulse(s) has been detected).

Accordingly, in the present specification, frequency division of a laser pulse through a multi-wavelength laser and an optical switch and time difference transmission of a wavelength division pulse train through delay control of a pulse using a DL We want to provide you with a technique. The back scattering pulse generated at this time is to improve the key rate by efficiently adjusting and blocking the pass wavelength of a tunable optical filter on a time-by-time basis.

In summary, the present specification relates to a technique for quantum key distribution (QKD) in a quantum secure communication system. More specifically, in a plug-and-play quantum key distribution system among QKD techniques, a key is efficiently distributed by using multi-path and multi-wavelength pulse trains generated by the light source on the bob side according to the lengths of channels and storage lines. It relates to a method, apparatus and system for increasing transmission rate. In addition, in the present specification, a method of efficiently switching a tunable optical filter according to the wavelength change time of a pulse is applied to suppress the increase in quantum bit error rate due to Rayleigh back scattering with only a minimum number of detectors and the length of a storage line. include method

If the problem to be solved in the present specification is described through the drawings, it may be as follows.

6 is an example of a time delay between pulse trains of the plug and play QKD technique.

In FIG. 6, in the plug-and-play quantum key distribution technology, a long length storage for the purpose of suppressing an increase in the key transmission error rate (= quantum bit error rate (QBER)) due to the occurrence of Rayleigh back-scattering An example in which the line SL is used and an example in which a pulse train is discontinuously generated at time intervals are shown, due to the long storage line and the pulse train discontinuously generated at time intervals. , a long delay time may occur as shown in the example of FIG. 6 .

In order to solve the above long delay time, in the present specification, a quantum cryptographic key distribution method that can have a high key rate by minimizing the delay time through a method of dividing the light source into multiple paths and wavelengths without using an additional laser diode , configure devices and systems.

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.

7 is a flowchart of a quantum cryptography communication method based on wavelength division and multipath according to an embodiment of the present specification.

According to FIG. 7 , the device may generate N first pulse trains each having a different wavelength (S710). Here, N may be a natural number. In addition, the device may mean a device at the bob side, and in this case, the device at the bob side may correspond to the previously described base station (of course, in this specification, the device at the bob side corresponds to the terminal). It is not intended to be excluded from the scope of this specification).

For example, the length of the first pulse train may be determined based on the length of at least one first pulse train, the number of source pulses included in one pulse train, the speed of light in an optical cable, and the repetition rate of a laser source. Here, the length of the first pulse train is determined based on the following equation, l = m*c*f_source (m), where l is the length of the first pulse train, and m is the source pulse included in the one pulse train. is the number of, c is the speed of light in the optical cable, and f_source may be the repetition rate of the laser source.

For convenience of description, a more specific example thereof will be described later.

The device may select k second pulse trains having different wavelengths from among the first pulse trains based on the length of the quantum channel and the length of the storage line (S720). Here, k is a natural number and k may be equal to or smaller than n.

For example, k, which is the number of second pulse trains, may be the number of maximum wavelengths selected by the device. Here, for example, the maximum number of wavelengths selected by the device may be determined based on the length of at least one quantum channel, the length of the storage line, and the length of the first pulse train.

, 상기 l_ch는 상기 양자 채널의 길이이고, 상기 l_sl은 상기 스토리지 라인의 길이이고, 및 상기 l은 상기 제1 펄스 열의 길이일 수 있다.As an example, the value of k, which is the maximum number of wavelengths selected by the device, is determined based on the following equation,

, wherein l_ch is the length of the quantum channel, l_sl is the length of the storage line, and l is the length of the first pulse train.

, 상기 l_ch는 상기 양자 채널의 길이고, 상기 l_sl은 상기 스토리지 라인의 길이고, 상기 l은 상기 제1 펄스 열의 길이고, 및 상기 a는 앞선 일례에 비해 줄어든 파장의 비율에 해당하는 값일 수 있다.As another example, compared to the previous example, wavelength division based on reduction of the length of the pulse train may be provided. For example, the value of k_2, which is the maximum number of wavelengths selected by the device, is determined based on the following equation,

, wherein l_ch is the length of the quantum channel, l_sl is the length of the storage line, l is the length of the first pulse train, and a is a value corresponding to a reduced wavelength ratio compared to the previous example.

For convenience of description, a more specific example thereof will be described later.

The device may transmit the k number of second pulse trains to the other device through the quantum channel based on the k number of multipaths (S730). Here, each of the k multipaths may include delay lines having different lengths.

For example, the value for the length of the delay line is a length corresponding to the length of at least one shortest delay line, the maximum number of wavelengths selected by the device, the length of the first pulse train, and a switching time required to change the wavelength component. can be determined based on

For example, the value for the length of the delay line is determined based on the following equation, the length of the delay line = t + (k - 1) * (l + l_st) (m), where t is the shortest delay line is the length of, k is the maximum number of wavelengths selected by the device, l is the length of the first pulse train, and l_st may be a length corresponding to a switching time required to change the wavelength component.

Meanwhile, for example, the method further includes filtering backscattering (backscattering) pulses generated based on transmission of the second pulse train based on a tunable optical filter, wherein the tunable optical filter selects a passable wavelength It may be an element that variably adjusts.

As described above, for example, the device may be a bob-side device, and the other device may be an Alice-side device. For example, the device may be a base station, and the other device may be a terminal (user equipment). ) can be.

For convenience of description, a more specific example thereof will be described later.

Hereinafter, the example of FIG. 7 will be described in more detail through drawings.

8 schematically illustrates a plug and play QKD protocol to be provided according to an embodiment of the present specification.

According to FIG. 8, in the present specification, in the technique of generating key information in quantum cryptographic communication, multi-paths of different lengths and multi-wavelength splitting methods are applied to pulses of light sources to minimize the length of storage lines and to next without delay as much as possible. We propose two schemes that allow pulse trains to be generated.

The first method increases the key rate by applying multipath and wavelength division in order to make the wavelengths between pulse trains different, and then minimizing the delay time of the current pulse train and the next pulse train.

In the second method, after splitting the pulse train of the first method into shorter lengths and applying different wavelengths to each pulse train, the length of the storage line can be reduced by the split ratio while having the same key rate as the first method.

In addition, in order to solve the problem of increasing errors due to Rayleigh back scattering pulses that may occur at this time, conventionally, a structure that allows each detector to detect pulses of different wavelengths has been used, and in this method, wavelength division A de-multiplexer (DE-MUX) and twice as many detectors as the number of pulse trains with different wavelengths may be required. Therefore, in the present specification, an increase in QBER is suppressed by disposing a tunable optical filter immediately before two detectors instead of applying multiple detectors and adjusting the wavelength of the filter according to a predetermined time interval.

1. First Method: Key Rate Improvement Scheme of Plug and Play QKD Protocol Through Multipath and Wavelength Splitting

The above specification proposes a method for minimizing the long time interval existing between pulse trains of a laser diode required to minimize Rayleigh back scattering. To this end, in this specification, the following wavelength and time division techniques are successively applied to make the wavelength and generation time of each pulse train different so that when a specific pulse train is completely transmitted, the next pulse train having a different wavelength is immediately transmitted. . In addition, the Rayleigh back scattering pulse that may occur at this time is blocked by placing a tunable optical filter at a position immediately before the detector, and the wavelength range of the filter is changed to block the remaining wavelengths except for the wavelength of the detection signal that changes with time. apply the method

Hereinafter, the present method will be described in more detail with reference to drawings.

9 schematically illustrates changes in a wavelength division, a time division structure, and a pulse train applied to a bob side according to an embodiment of the present specification.

Referring to FIG. 9 , a generator (eg, a multi-wavelength (MW) generator) 910 may generate N pulse trains as described above. Here, m pulses may be included in one pulse train, and each of the N pulse trains may have a different wavelength. Examples like this are shown in ① of FIG. 9 .

The optical switch (OSW) 920 may select as many K pulse trains among N pulse trains. Examples like this are shown in ② of FIG. 9 .

Meanwhile, the selected K pulse trains may be transmitted through the multipath 930. Examples like this are shown in ③ of FIG. 9 .

Hereinafter, a detailed operation of each part/element will be described.

1.1. Wavelength division based on channel and SL length: ①→② in FIG.

In the wavelength division step of the transmission pulse train generated by the light source, pulses having n different wavelengths are first generated in a multi-wavelength generator. The multi-wavelength generator that can be applied at this time is mainly a method using the nonlinear optical effect of a gain material such as an Erbium-doped fiber or a Raman fiber. It is possible to simultaneously generate n pulses having a difference. Thereafter, pulse signals having k (n≥k) different wavelengths that can be used as maximum based on the length of the channel and the length of the storage line among the pulse signals having n different wavelengths are transmitted through the optical switch osw. choose through

At this time, the maximum number k of the selected wavelengths is determined by the length of the storage line and the channel occupying most of the entire transmission/reception path, and its value can be obtained as follows.

[Equation 11]

Here, l is the length of one pulse train generated by the light source, m is the number of source pulses included in one pulse train, c = 2×10^8 (m/s) is the speed of light in the optical cable, f_source may be the repetition rate of the laser source.

[Equation 12]

Here, k may be the maximum number of wavelengths selectable by the optical switch, l_ch may be the length of a quantum channel, and l_sl may be the length of a storage line.

The pulse generated by the laser diode of the plug-and-play qkd technology passes through the channel and storage line, which occupy most of the transmission and reception path, twice during the round trip from Bob to Alice to Bob. Therefore, the total round-trip length of the route can be set to 2*(l_ch+l_sl). (Except since the length of the path used for the rest of Alice and Bob's optical elements and internal configuration is very short)

If it is assumed that a pulse train composed of m pulses with length l is sent through a round-trip transmission/reception path, in the conventional technique, one pulse train passes through a round-trip path to prevent an increase in error due to detection of back scattering pulses to the detector. After being detected through the pulse, the next pulse can be transmitted.

Therefore, a long delay time must be waited for transmission of the next pulse train. In the present specification to solve this problem, since the method of applying a method of directly transmitting the next pulse train after the current pulse train is transmitted, up to k pulse trains having different wavelengths can be used to fill the round-trip path can be known through the above expression. Accordingly, in the present specification, pulse trains having up to k different wavelengths may be selected using an optical switch.

1.2. Time division through multiple paths including delay lines of different lengths: ②→③ in FIG.

K pulse trains having different wavelengths selected through an optical switch are formed at the same location as shown in ② of FIG. 9 . In the time division step, a difference in length of a path through which each pulse train passes is generated to distinguish a transmission time of each pulse train. Through this, continuous transmission is possible without overlapping between pulse trains and time delay between previously generated pulse trains having different wavelengths.

Here, the lengths of delay lines (DLs) of k multipaths may be described through the drawings as follows.

10 schematically illustrates an example of setting delay line lengths of k multipaths.

According to FIG. 10 , the length of the first path may be defined as, for example, t (m), and the length of the second path may be defined as, for example, t+(l+l_st) (m). Similarly, the length of the k-th path may be defined as, for example, t+(k-1)*(l+l_st) (m).

That is, the difference in transmission time of the pulse train passing through each path is generated through the difference in the length of the delay line. It is set equal to the sum of the lengths (l_st = t_sw c) corresponding to the switching time (t_sw) required for change.

[Equation 13]

Here, l may be a difference in length of the delay line.

In this specification, after one pulse train is transmitted, the switching time for changing the wavelength to be blocked in the tunable optical filter used to suppress back scattering pulses that may appear when the next pulse train is transmitted immediately after has elapsed This is because the next pulse train with a different wavelength can be received by the detector only after the

Therefore, assuming a situation in which pulse trains having k different wavelengths are continuously sent, the length of the kth path from the first path is l+l_st when the length of the shortest path among all paths in FIG. 10 is t, -> It is configured to increase by the path difference as many as the multiple of Δl.

1.3. Blocking of Rayleigh backscattering pulses with tunable optical filters

11 schematically illustrates an application example of an optical filter for removing back scattering pulses.

In the present specification, pulse trains having different wavelengths may be continuously connected and transmitted through the previously presented wavelength division part and time division part. However, in order to apply this method, an optical filtering process as shown in FIG. 11 to prevent backscattering pulses from being detected by the detector must be additionally applied just before the single photon detector (SPD). To do this, two methods can be considered.

12 schematically illustrates an example of a configuration of a detection unit to which two MUXs and 2k SPDs are applied.

As shown in the configuration in FIG. 12, after sending a signal to a different path according to the wavelength of the signal coming into the De-Mux using the De-Mux and a detector corresponding to twice the number of wavelengths used, the corresponding wavelength can be detected. It can be detected by connecting it to a detector capable of detecting it. In this case, since each detector detects only a signal of a corresponding wavelength, an increase in error rate due to detection of the back-scattering pulse can be prevented through this. However, this method has a disadvantage in that many detectors may be required because different detectors must be used for each wavelength.

Therefore, in this specification, after measuring the time it takes for the first pulse train generated by the laser diode to return from the Bob side through the quantum channel and the Alice side to the point where detection starts in the single photon detector, In the tunable optical filter installed immediately before the photon detector, a configuration is provided in which the wavelength of the filter is adjusted so that only the wavelength of the first pulse passes, and all pulses of the remaining wavelengths are blocked.

And at the point of time when all m pulses from the first pulse to the last pulse of the first pulse train pass through, the wavelength of the second pulse train that can pass through the filter during the switching time (t_sw) before the pulse of the next wavelength enters. Change the wavelength range to suit. Then, this process is repeated k times until all k pulse trains with different wavelengths pass through.

After that, the laser diode transmits the pulse train of the first wavelength again, so the previous process is repeated from the beginning. Through this process, a back scattering pulse generated by pulses of different wavelengths can be effectively blocked through a single photon detector.

2. Method 2: Alice-Side Miniaturization and Key Rate Improvement Scheme of Plug and Play QKD Protocol through Minimization of Storage Line Length

Since the basic structure of this specification follows the same basic structure as the first specification technique, the differences will be mainly described.

In the first specification technique presented above, in order to remove the effect of Rayleigh back-scattering, the same length (l_sl) of the storage line SL as the existing plug and play technique is used on the Alice side. And the length corresponds to the length that can store the pulse train of one block generated by the light source on Bob's side, and can be expressed as the following equation.

[Equation 14]

Here, the speed of the optical pulse in the fiber may be 2×10^8 (m/s). In addition, f_laser may mean a repetition rate of a source.

As can be seen from the above equation, in order to reduce the length of sl, assuming that the repetition rate of the light source is limited, it is required to reduce the number of pulses included in one pulse train. Therefore, in the present specification, a technique capable of improving a key rate by reducing the number of pulses included in one pulse train by a certain percentage compared to the first technique and dividing more wavelengths is proposed.

2.1. Wavelength division part through length reduction of pulse train

13 schematically illustrates changes in wavelength division, time division structure, and pulse train applied to a bob side according to another embodiment of the present specification.

In the above specification, compared to the first technique (Fig. 13(a)), the length of the pulse train generated by the MW-generator (= the number of pulses included in a single pulse train) is reduced by a times as shown in Fig. 13(b) A pulse train having more diverse wavelengths by the reduced ratio is selected through an optical switch.

In this process, an additional optical switch (the number of channels used in the optical switch is increased) is used to select a pulse train with more wavelengths. Through this, the length of the storage line on the Alice side can be reduced by a times compared to the first method.

At this time, due to the decrease in the length of the storage line (l_sl → l_sl/a) and the decrease in the length of one pulse train (l → l/a), the maximum number of pulse trains that can fill the round-trip path is changed as follows.

[Equation 15]

Here, k_2 may mean the maximum number of wavelengths selectable by the optical switch, l_ch may mean the length of a quantum channel, l_sl may mean the length of a storage line, and l may mean the length of one pulse train generated through a light source. there is.

Therefore, after generating as many k_2 pulse trains, a process of transmitting them through a multi-path having a delay line is performed in the same manner as in the first technique. After all k_2 pulse trains have been transmitted through this process, the same process is repeatedly performed from the beginning.

2.2. Tunable optical filter control and detection part of received key information

In the above specification, the wavelength of the optical filter must be adjusted more frequently than Method 1 due to the decrease in the length of the pulse train generated by Bob and the increase in the number of pulse trains having different wavelengths. Accordingly, the present technique consumes more pass wavelength conversion time of the optical filter than the first technique, but the key rate is hardly reduced because the wavelength conversion time of currently generally used filters is about several nanoseconds (ns).

FIG. 14 schematically shows the difference between methods of detecting key information of the same length in the first technique and the second technique.

In the above specification, the time t(λ_j') required for the jth pulse train (having a wavelength of λ_j', assuming that the length is set a times shorter than the first technique) passes is the time t(λ_j) required for the first technique Since it is a times shorter than that of , the conversion cycle of the optical filter must be set to a cycle of 1/a compared to the conventional one. In addition, the time (t_sw) required for conversion of the pass wavelength of the filter may be the same for both techniques.

From this, it can be seen that the time required for the detector to receive the pulse train including m pulses and the wavelength of λ_1 in the first technique is t(λ_1)+t_sw.

임을 알 수 있다. 따라서 두 번째 방법에서는 첫 번째 기법에서 사용한 것과 동일한 길이의 펄스 열을 검출하는데 (a-1)·t_sw의 시간이 더 소모된다.On the other hand, the second technique may require the following time to detect the same number of m pulses as the first technique. In the second method, it is assumed that the length obtained by dividing the pulse train of the first method a equally is used as the length of the basic pulse train. In this case, the total time taken by the second method to detect a pulse train having the same length as the first method is

It can be seen that Therefore, in the second method, it takes (a-1) t_sw more time to detect the pulse train with the same length as that used in the first method.

In the key information detection unit, as shown in FIG. 14, in the first technique, h pieces of information detected with a specific wavelength (λ_1) are stored in memory and the value is used as key information for one block. On the other hand, in the second technique, in order to return the shortened pulse train length of one block, h pieces of information collected from key train information generated from a pulse trains, which are the length ratios of the pulse trains shortened by the signal generator, are used as key values.

In the technique of this specification described so far, a low shifted key, which was a problem of the existing plug-and-play quantum cryptography communication technique, is commonly applied to a pulse train generated from a light source by applying wavelength division, multipath technique, and optical filter switching technique. There may be an effect of improving the rate.

In the conventional technique, after generating one pulse train, traveling back and forth between the channel and the SL, waiting for the next pulse until it is detected, the shifted key rate of the conventional technique is l_sl/((l_channel+l_sl) n_sw) additionally has a key rate loss. However, since transmission of the next pulse train is possible without a delay time in the technique of the present specification, the key rate can be improved, and the improvement ratio can be equal to ((l_channel + l_sl)·n_sw)/l_sl.

If these are explained based on the equation, it may be as follows.

A. Shifted key rate of the existing plug-and-play QKD technique (=R_conv)

[Equation 16]

- L_Channel: Channel length

- L_SL: Storage line length

- f: pulse repetition rate

- μ: average number of photons

- n_f: fiber transmission coefficient

- n_f = 10 ^ (-α_1 * L_channel / 10)), where (α_1: fiber loss)

- n_B: total loss in rice

- n_B=10^(-L_Bob/10))

- n_D: detection efficiency of SPD

B. Shifted key rate of the technique herein (=R_prop)

[Equation 17]

Here, the optical filter coefficient n_sw may be 10^(-α_f/10), and α_f may mean a filter loss.

In the second technique, the length of the storage line, which was the biggest obstacle in reducing the weight of the Alice side in the plug-and-play quantum cryptography communication technique, is reduced by dividing the basic pulse train generated by the Bob side into smaller length units and then applying the wavelength division technique. can be minimized.

The effect of reducing the length of the storage line can be reduced by the ratio of reducing the number of pulses included in one pulse train in the equation below. For example, if the length of the storage line was determined in units of 500 pulses in the existing method, the proposed method divides them into 10 equal parts and transmits each 50 pulse trains through a pulse train having a different wavelength. Reduce the length of the storage line to 1/10.

[Equation 18]

The example of FIG. 7 has been described so far. Hereinafter, the example of FIG. 7 will be described in a different way.

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.

15 is a flowchart of a quantum cryptography communication method based on wavelength division and multipath by a device in a bob side according to an embodiment of the present specification.

According to FIG. 15 , the device may generate N first pulse trains each having a different wavelength (S1510). Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

Based on the length of the quantum channel and the length of the storage line, the device may select K second pulse trains having different wavelengths from among the first pulse trains (S1520). Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

Thereafter, the device may transmit K second pulse trains to another device through a quantum channel based on K multipaths (S1530). Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

16 schematically illustrates a block diagram of a quantum cryptography communication device based on wavelength splitting and multipath by a device in a bob side according to an embodiment of the present specification.

According to FIG. 16 , an apparatus 1600 may include a generator 1610, an optical switch 1620, and a multipath 1630. Here, the device 1600 may be connected to the device of the Alice side described above through the quantum channel 1640.

Meanwhile, a control device (eg, may include a processor) 1670 for controlling the device may exist separately from the device. The control device 1670 may include a generator control unit 1650 and an optical switch control unit 1660.

If the above description is applied to the above device 1600, it may be as follows.

The generator 1610 may generate N first pulse trains each having a different wavelength. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

The optical switch 1620 may select K second pulse trains having different wavelengths from among the first pulse trains based on the length of the quantum channel and the length of the storage line. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of explanation.

Meanwhile, each of the K second pulse trains may pass through the multipath 1630 . Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

On the other hand, the above control device or processor may be configured as follows.

The generator controller 1650 may be configured to generate N first pulse trains each having a different wavelength. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

The optical switch controller 1660 may be configured to select K second pulse trains having different wavelengths from among the first pulse trains based on the length of the quantum channel and the length of the storage line. Here, the K number of the second pulse trains may be configured to be transmitted to the other device through the quantum channel based on the K number of multipaths. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

On the other hand, the device provided in this specification may include a processor for controlling the device provided in this specification, and this may be described through the drawings as follows.

FIG. 17 schematically illustrates a block diagram of a quantum cryptography communication device based on wavelength splitting and multipath by a device in a bob side according to another embodiment of the present specification.

According to FIG. 17 , an apparatus 1700 may include a generator 1710, an optical switch 1720, and a multipath 1730. Here, the device 1700 may be connected to the device of the Alice side described above through the quantum channel 1740.

Meanwhile, a control device (eg, may include a processor) 1770 for controlling the device may be included in the device. The control device 1770 may include a generator control unit 1750 and an optical switch control unit 1760.

If the above description is applied to the device 1700, it may be as follows.

The generator 1710 may generate N first pulse trains each having a different wavelength. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of explanation.

The optical switch 1720 may select K second pulse trains having different wavelengths from among the first pulse trains based on the length of the quantum channel and the length of the storage line. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of explanation.

Meanwhile, each of the K second pulse trains may pass through the multipath 1730 . Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of explanation.

On the other hand, the above control device or processor may be configured as follows.

The generator controller 1750 may be configured to generate N first pulse trains each having a different wavelength. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of description.

The optical switch controller 1760 may be configured to select K second pulse trains having different wavelengths from among the first pulse trains based on the length of the quantum channel and the length of the storage line. Here, the K number of the second pulse trains may be configured to be transmitted to the other device through the quantum channel based on the K number of multipaths. Here, since a more specific example for this is as described above, repetition of overlapping content will be omitted for convenience of explanation.

18 illustrates a communication system 1 applied to this specification.

Referring to FIG. 18, a communication system 1 applied to the present 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.

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 4 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 5 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. 19 illustrates a wireless device to which the present specification can be applied.

Referring to FIG. 19 , the first wireless device 100 and the 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. 18 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.

20 shows another example of a wireless device applicable to the present specification.

20, 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. 19 and the example of the wireless device in FIG. 20, in FIG. 19, the processors 102 and 202 and the memories 104 and 204 are separated, but in the example of FIG. 20, 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.

21 illustrates a signal processing circuit for a transmission signal.

Referring to FIG. 21 , 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. 21 may be performed by processors 102 and 202 and/or transceivers 106 and 206 of FIG. 19 . The hardware elements of FIG. 21 may be implemented in processors 102 and 202 and/or transceivers 106 and 206 of FIG. 19 . For example, blocks 1010-1060 may be implemented in processors 102 and 202 of FIG. 19 . Also, blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 19 , and block 1060 may be implemented in the transceivers 106 and 206 of FIG. 19 .

The codeword may be converted into a radio signal through the signal processing circuit 1000 of FIG. 21 . 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. 21 . For example, wireless devices (eg, 100 and 200 of FIG. 19 ) 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.

22 shows another example of a wireless device applied to the present specification. A wireless device may be implemented in various forms according to use-case/service (see FIG. 18).

Referring to FIG. 22, wireless devices 100 and 200 correspond to the wireless devices 100 and 200 of FIG. 19, 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. 19 and/or one or more memories 104, 204. For example, transceiver(s) 114 may include one or more transceivers 106, 206 of FIG. 19 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 control unit 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. 18, 100a), a vehicle (Fig. 18, 100b-1, 100b-2), an XR device (Fig. 18, 100c), a mobile device (Fig. 18, 100d), a home appliance. (FIG. 18, 100e), IoT device (FIG. 18, 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. 18, 400), a base station (Fig. 18, 200), a network node, and the like. Wireless devices can be mobile or used in a fixed location depending on the use-case/service.

In FIG. 22 , 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, the implementation example of FIG. 22 will be described in more detail with reference to drawings.

23 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. 23, 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. 22 .

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.

24 illustrates a vehicle or autonomous vehicle applied to 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. 24, 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. 22 .

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.

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.

25 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. 25 , 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 .

26 illustrates an XR device 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. 26, 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.

27 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. 27 , 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.

28 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. 28, 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.

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 6 below. That is, Table 6 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 can have key factors such as access network congestion and enhanced data security.

29 is a diagram showing an example of a communication structure that can be provided in a 6G system.

6G systems are expected to have 50 times higher simultaneous radiocommunication connectivity than 5G radiocommunication systems. 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. New network characteristics in 6G may be as follows.

- Satellites integrated network: 6G is expected to be integrated with satellites to serve the 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 communications systems, 6G is revolutionary and will update the evolution of wireless from “connected things” to “connected intelligence.” AI can be applied at each step of the communication procedure (or each procedure of signal processing to be described later).

- 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 machine-to-machine, 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 classified 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.

30 schematically illustrates an example of a perceptron structure.

Referring to FIG. 30, when the input vector x=(x1,x2,...,xd) is input, each component is multiplied by a weight (W1,W2,...,Wd), and after summing 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. 30 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. 30 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.

31 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. 31, three layers are disclosed, but when counting the number of actual artificial neural network layers, since the count excludes the input layer, it can be seen as a total of two layers. The 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).

32 schematically illustrates a deep neural network example.

The deep neural network shown in FIG. 32 is a multi-layer perceptron composed 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.

33 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. 33, 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. 33). In this case, since a weight is added for each connection in the connection process from one input node to the hidden layer, a total of h×w weights must be considered. Since there are h×w 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. 33 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. 34 As shown in , weighted sum and activation function calculations are performed for overlapping filters.

34 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. 34 , a 3×3 filter is applied to the top-left 3×3 region of the input layer, and an output value obtained by performing a weighted sum and an activation function operation on 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.

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

Referring to FIG. 35, 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.

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

Referring to FIG. 36, the recurrent neural network operates in a predetermined sequence of 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 arranged in a recurrent neural network, it 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. Adding to the Sub-THz band mmWave band will increase 6G cellular communications capacity. Among 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.

37 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. UAVs 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 1 call-to-noise ratio, interference prevention 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.

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

As shown in FIG. 38, 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 7 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. 39 is a diagram illustrating an example of an electronic element-based THz wireless communication transceiver.

Methods of generating THz using electronic devices include a method using a semiconductor device such as a Resonant Tunneling Diode (RTD), a method using a local oscillator and a multiplier, and an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor). There is a MMIC (Monolithic Microwave Integrated Circuits) method using , a method using a Si-CMOS-based integrated circuit, and the like. In the case of FIG. 39, a doubler, tripler, or 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. 39 . In FIG. 39, IF represents an intermediate frequency, tripler and multipler represent multipliers, PA power amplifiers, LNAs are low noise amplifiers, and PLLs are phase-locked circuits. -Locked Loop).

40 is a diagram illustrating an example of a method for generating a THz signal based on an optical device, and FIG. 41 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. 40, a laser diode, a broadband optical modulator, and a high-speed photodetector are required to generate a THz signal based on an optical device. In the case of FIG. 40 , 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. 40, 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. In FIG. 41, 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, light-to-optical 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. 42 and 43 . 42 illustrates the structure of a photoinc source-based transmitter, and FIG. 43 illustrates the 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).

Claims (15)

A method for performing communication with another device through a quantum channel, performed by a device, comprising:
Generating n first pulse trains each having a different wavelength,
wherein n is a natural number;
Based on the length of the quantum channel and the length of the storage line, select k second pulse trains having different wavelengths from among the first pulse trains,
k is a natural number and k is equal to or less than n; and
Transmitting the k second pulse trains to the other device through the quantum channel based on the k multipaths;
Each of the k multipaths is composed of delay lines having different lengths. The method of claim 1, wherein the length of the first pulse train is based on the length of at least one first pulse train, the number of source pulses included in one pulse train, the speed of light in an optical cable, and the repetition rate of a laser source characterized in that it is determined. The method of claim 2, wherein the length of the first pulse train is determined based on the following equation,
l = m*c*f_source (m)
Where l is the length of the first pulse train,
m is the number of source pulses included in the one pulse train;
c is the speed of light in the optical cable, and
Wherein f_source is the repetition rate of the laser source. The method of claim 1, wherein k, the number of second pulse trains, is the number of maximum wavelengths selected by the device. 5. The method of claim 4, wherein the number of maximum wavelengths selected by the device is determined based on a length of at least one quantum channel, a length of the storage line, and a length of the first pulse train. The method of claim 5, wherein the value of k, which is the maximum number of wavelengths selected by the device, is determined based on the following equation,

The l_ch is the length of the quantum channel,
The l_sl is the length of the storage line, and
Wherein l is the length of the first pulse train. The method of claim 5, wherein the value of k_2, which is the maximum number of wavelengths selected by the device, is determined based on the following formula,

The l_ch is the length of the quantum channel,
The l_sl is the length of the storage line,
l is the length of the first pulse train, and
Wherein a is a positive integer. The method of claim 1 , wherein the value for the length of the delay line is based on a length of at least one shortest delay line, a maximum number of wavelengths selected by the device, a length of the first pulse train, and a switching time required to change the wavelength component. Characterized in that it is determined based on the corresponding length. The method of claim 8, wherein a value for the length of the delay line is determined based on the following formula,
Length of delay line = t + (k - 1)*(l + l_st) (m)
t is the length of the shortest delay line,
The k is the number of maximum wavelengths selected by the device,
Where l is the length of the first pulse train, and
Wherein l_st is a length corresponding to a switching time required to change the wavelength component. The method of claim 1, wherein the method,
Further comprising filtering a backscattering pulse generated based on transmission of the second pulse train based on a tunable optical filter,
The method characterized in that the tunable optical filter is an element that variably adjusts the passable wavelength. 2. The method of claim 1, wherein the device is a bob-side device and the other device is an alice-side device. The method of claim 1, wherein the device is a base station, and the other device is a user equipment. device,
a generator for generating n first pulse trains each having a different wavelength;
an optical switch for selecting k second pulse trains having different wavelengths from among the first pulse trains based on the lengths of the quantum channels and the storage lines; and
Including the k multipaths through which each of the k second pulse trains passes,
wherein n is a natural number,
wherein k is a natural number and k is equal to or smaller than n;
Each of the k multipaths is composed of delay lines having different lengths. device,
at least one memory; and
at least one processor operably coupled to the at least one memory, the processor comprising:
configured to generate n first pulse trains each having a different wavelength; and
Based on the length of the quantum channel and the length of the storage line, k second pulse trains having different wavelengths are selected from among the first pulse trains,
The k second pulse trains are configured to be transmitted to the other device through the quantum channel based on the k multipaths,
wherein n is a natural number,
wherein k is a natural number and k is equal to or smaller than n;
Each of the k multipaths is composed of delay lines having different lengths. In at least one computer readable medium containing instructions based on being executed by at least one processor,
It is configured to generate n first pulse trains each having a different wavelength,
wherein n is a natural number;
Based on the length of the quantum channel and the length of the storage line, k second pulse trains having different wavelengths are selected from among the first pulse trains,
k is a natural number and k is equal to or less than n; and
And configured to transmit the k second pulse trains to the other device through the quantum channel based on the k multipaths,
Each of the k multipaths is composed of delay lines having different lengths.

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