KR20230031892A - Method for transmitting and receiving signals in a wireless communication system using an auto-encoder and apparatus therefor - Google Patents

Abstract

The present specification provides a method for transmitting and receiving signals in a wireless communication system using an auto-encoder.
More specifically, the method performed by the receiving end includes receiving the first weight of the encoder neural network of the sending end from the sending end; learning the first weight and the second weight of the receiving end encoder neural network based on a gradient descent method; transmitting update weight information updated for the first weight based on the learning to the transmitter; and receiving a signal to which the updated weight information is applied from the transmitter.

Description

Method for transmitting and receiving signals in a wireless communication system using an auto-encoder and apparatus therefor

The present specification relates to transmission and reception of signals using an auto-encoder, and more particularly, to a method and apparatus for transmitting and receiving a signal in a wireless communication system using an auto-encoder.

A wireless communication system is widely deployed to provide various types of communication services such as voice and data. In general, a wireless communication system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of the multiple access system include a Code Division Multiple Access (CDMA) system, a Frequency Division Multiple Access (FDMA) system, a Time Division Multiple Access (TDMA) system, a Space Division Multiple Access (SDMA) system, and an Orthogonal Frequency Division Multiple Access (OFDMA) system. , SC-FDMA (Single Carrier Frequency Division Multiple Access) system, IDMA (Interleave Division Multiple Access) system, and the like.

An object of the present specification is to provide a neural net-based automatic artificial intelligence transmission/reception method and apparatus for automatically adapting to the current situation with minimal feedback signaling so as to be suitable for various devices and environments.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

In the present specification, in a method for transmitting and receiving signals in a wireless communication system using an auto encoder, the method performed by a receiving end includes receiving a first weight of an encoder neural network of a transmitting end from a transmitting end. ; learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method; transmitting update weight information updated for the first weight based on the learning to the transmitter; and receiving a signal to which the updated weight information is applied from the transmitter.

In the present specification, the learning based on the gradient descent method is characterized in that learning is performed using a difference between a transmission symbol of a transmission signal and a reception symbol of a reception signal for the transmission signal.

In addition, the method according to the present specification is characterized in that it further comprises the step of applying the update weight information to the receiving end encoder neural net.

In addition, in the present specification, the update weight information is characterized in that a difference value between a previous weight value of the first weight and a new weight value of the first weight.

In addition, the method in the present specification is characterized in that it further comprises transmitting neural network structure information on at least one of the structure of the transmitter encoder neural net and the structure of the receiver encoder neural net to the transmitter.

In addition, in this specification, the neural network structure information is transmitted to the transmitter together with the update weight information.

In addition, in the present specification, the neural network structure information is characterized in that it is transmitted to the transmitter when at least one of the structure of the transmitter encoder neural net and the structure of the receiver encoder neural net is changed.

In addition, in this specification, the structure of the transmitter encoder neural net or the structure of the receiver encoder neural net is characterized in that it is changed when a new node is added to a specific layer of the neural network or when an arbitrary node is removed.

In addition, the present specification provides a receiver for transmitting and receiving a signal in a wireless communication system using an auto encoder, including a transmitter for transmitting a radio signal; a receiver for receiving a radio signal;

at least one processor; and at least one computer memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising: a transmit end encoder; Receiving a first weight of a neural network from a transmitter; learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method; transmitting update weight information updated for the first weight based on the learning to the transmitter; and receiving a signal to which the updated weight information is applied from the transmitter.

The present specification has an effect of transmitting and receiving minimum feedback by making it suitable for various devices and environments through a neural net-based automatic artificial intelligence transmission and reception method that adapts itself to a specific situation.

The effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. .

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to aid understanding of the present invention, provide examples of the present invention and, together with the detailed description, describe the technical features of the present invention.
1 illustrates physical channels and typical signal transmission used in a 3GPP system.
2 is a diagram showing an example of a communication structure that can be provided in a 6G system.
3 is a diagram showing an example of a perceptron structure.
4 is a diagram showing an example of a multilayer perceptron structure.
5 is a diagram showing an example of a deep neural network.
6 is a diagram showing an example of a convolutional neural network.
7 is a diagram showing an example of a filter operation in a convolutional neural network.
8 shows an example of a neural network structure in which a cyclic loop exists.
9 shows an example of an operating structure of a recurrent neural network.
10 is a diagram showing an example of an electromagnetic spectrum.
11 is a diagram showing an example of a THz communication application.
12 is a diagram illustrating an example of an electronic element-based THz wireless communication transceiver.
13 is a diagram illustrating an example of a method of generating a THz signal based on an optical element.
14 is a diagram showing an example of an optical element-based THz wireless communication transceiver.
15 is a diagram illustrating the structure of a photoinc source-based transmitter.
16 is a diagram illustrating a structure of an optical modulator.
17 shows an example of a wireless communication system for an auto encoder transmission/reception method.
18 is a diagram showing an example of a wireless communication system for a transmission/reception method of an auto-encoder proposed in this specification.
19 is a diagram showing another example of a wireless communication system for the method of transmitting and receiving an auto encoder proposed in this specification.
20 is a diagram showing another example of a wireless communication system for a transmission/reception method of an auto-encoder proposed in this specification.
21 is a diagram illustrating an example of a recycling weight feedback method as shown in FIG. 21 proposed in this specification.
22 is a diagram showing an example of a NettoNet addition operation proposed in this specification.
23 is a flowchart illustrating an example of a method for transmitting and receiving signals in a wireless communication system using an auto-encoder proposed in this specification.
24 illustrates a communication system applied to the present invention.
25 illustrates a wireless device applicable to the present invention.
26 illustrates a signal processing circuit for a transmission signal.
27 shows another example of a wireless device applied to the present invention.
28 illustrates a portable device applied to the present invention.
29 illustrates a vehicle or autonomous vehicle to which the present invention is applied.
30 illustrates a vehicle to which the present invention is applied.
31 illustrates an XR device applied to the present invention.
32 illustrates a robot applied to the present invention.
33 illustrates an AI device applied to the present invention.

The following techniques may be used in various radio access systems such as CDMA, FDMA, TDMA, OFDMA, SC-FDMA, and the like. CDMA may be implemented with a radio technology such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA may be implemented with a radio technology such as Global System for Mobile communications (GSM)/General Packet Radio Service (GPRS)/Enhanced Data Rates for GSM Evolution (EDGE). OFDMA may be implemented with radio technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and Evolved UTRA (E-UTRA). UTRA is part of the Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a part of Evolved UMTS (E-UMTS) using E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE/LTE-A/LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

For clarity of explanation, the description is based on a 3GPP communication system (eg, LTE, NR, etc.), but the technical spirit of the present invention is not limited thereto. LTE refers to technology after 3GPP TS 36.xxx Release 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 is referred to as LTE-A pro. 3GPP NR refers to technology after TS 38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17 and/or Release 18. "xxx" means standard document detail number. LTE/NR/6G may be collectively referred to as a 3GPP system. For background art, terms, abbreviations, etc. used in the description of the present invention, reference may be made to matters described in standard documents published prior to the present invention. For example, you can refer to the following document.

3GPP LTE

- 36.211: Physical channels and modulation

- 36.212: Multiplexing and channel coding

- 36.213: Physical layer procedures

- 36.300: Overall description

- 36.331: Radio Resource Control (RRC)

3GPP NRs

- 38.211: Physical channels and modulation

- 38.212: Multiplexing and channel coding

- 38.213: Physical layer procedures for control

- 38.214: Physical layer procedures for data

- 38.300: NR and NG-RAN Overall Description

- 38.331: Radio Resource Control (RRC) protocol specification

Physical Channels and Frame Structure

Physical channels and general signal transmission

1 illustrates physical channels and typical signal transmission used in a 3GPP system. In a wireless communication system, a terminal receives information from a base station through downlink (DL), and the terminal transmits information to the base station through uplink (UL). Information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist according to the type/use of the information transmitted and received by the base station and the terminal.

When the terminal is turned on or newly enters a cell, the terminal performs an initial cell search operation such as synchronizing with the base station (S11). To this end, the terminal may receive a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station to synchronize with the base station and obtain information such as a cell ID. After that, the terminal can acquire intra-cell broadcast information by receiving a physical broadcast channel (PBCH) from the base station. Meanwhile, the terminal may check the downlink channel state by receiving a downlink reference signal (DL RS) in the initial cell search step.

After completing the initial cell search, the UE acquires more detailed system information by receiving a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Shared Channel (PDSCH) according to the information carried on the PDCCH. It can (S12).

Meanwhile, when accessing the base station for the first time or when there is no radio resource for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (S13 to S16). To this end, the UE transmits a specific sequence as a preamble through a physical random access channel (PRACH) (S13 and S15), and responds to the preamble through a PDCCH and a corresponding PDSCH (RAR (Random Access Channel) Response message) may be received In the case of contention-based RACH, a contention resolution procedure may be additionally performed (S16).

After performing the above procedure, the UE receives PDCCH/PDSCH (S17) and physical uplink shared channel (PUSCH)/physical uplink control channel (PUSCH) as a general uplink/downlink signal transmission procedure. Control Channel; PUCCH) transmission (S18) may be performed. In particular, the terminal may receive downlink control information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats may be applied depending on the purpose of use.

On the other hand, the control information that the terminal transmits to the base station through the uplink or the terminal receives from the base station is a downlink / uplink ACK / NACK signal, CQI (Channel Quality Indicator), PMI (Precoding Matrix Index), RI (Rank Indicator) ) and the like. The UE may transmit control information such as the aforementioned CQI/PMI/RI through PUSCH and/or PUCCH.

Structure of uplink and downlink channels

Downlink channel structure

The base station transmits a related signal to the terminal through a downlink channel described later, and the terminal receives the related signal from the base station through a downlink channel described later.

(1) Physical Downlink Shared Channel (PDSCH)

PDSCH carries downlink data (eg, DL-shared channel transport block, DL-SCH TB), and modulation methods such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and 256 QAM are used. Applied. A codeword is generated by encoding the TB. PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and modulation symbols generated from each codeword are mapped to one or more layers (Layer mapping). Each layer is mapped to a resource along with a demodulation reference signal (DMRS), generated as an OFDM symbol signal, and transmitted through a corresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

The PDCCH carries downlink control information (DCI) and a QPSK modulation method or the like is applied. One PDCCH is composed of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) according to an Aggregation Level (AL). One CCE is composed of 6 REGs (Resource Element Groups). One REG is defined as one OFDM symbol and one (P)RB.

The UE obtains DCI transmitted through the PDCCH by performing decoding (aka, blind decoding) on a set of PDCCH candidates. A set of PDCCH candidates decoded by the UE is defined as a PDCCH search space set. The search space set may be a common search space or a UE-specific search space. The UE may obtain DCI by monitoring PDCCH candidates in one or more search space sets configured by MIB or higher layer signaling.

Uplink channel structure

The terminal transmits a related signal to the base station through an uplink channel described later, and the base station receives the related signal from the terminal through an uplink channel described later.

(1) Physical Uplink Shared Channel (PUSCH)

PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and/or uplink control information (UCI), and CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform , Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) waveform. When the PUSCH is transmitted based on the DFT-s-OFDM waveform, the UE transmits the PUSCH by applying transform precoding. For example, when transform precoding is impossible (eg, transform precoding is disabled), the terminal transmits a PUSCH based on the CP-OFDM waveform, and when transform precoding is possible (eg, transform precoding is enabled), the terminal transmits the CP-OFDM The PUSCH may be transmitted based on a waveform or a DFT-s-OFDM waveform. PUSCH transmission is dynamically scheduled by the UL grant in DCI or semi-static based on higher layer (eg, RRC) signaling (and/or Layer 1 (L1) signaling (eg, PDCCH)) It can be scheduled (configured grant). PUSCH transmission may be performed on a codebook basis or a non-codebook basis.

(2) Physical Uplink Control Channel (PUCCH)

PUCCH carries uplink control information, HARQ-ACK and/or scheduling request (SR), and may be divided into multiple PUCCHs according to PUCCH transmission length.

6G system general

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

6G systems include Enhanced mobile broadband (eMBB), Ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, Tactile internet, High throughput, High network capacity, High energy efficiency, Low backhaul and It may have key factors such as access network congestion and enhanced data security. FIG. 2 is a diagram illustrating 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 communication systems, 6G is revolutionary and will update the wireless evolution from “connected things” to “connected intelligence.” each procedure of processing).

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

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

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

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

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

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

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

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

Core implementation technology of 6G system

Artificial Intelligence

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

Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in 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.

Referring to FIG. 3, when the input vector x=(x1,x2,...,xd) is input, each component is multiplied by a weight (W1,W2,...,Wd), and after summing up the results, The entire process of applying the activation function σ() is called a perceptron. The huge artificial neural network structure may extend the simplified perceptron structure shown in FIG. 3 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. 3 can be described as being composed of a total of three layers based on input values and output values. An artificial neural network in which H number of (d + 1) dimensional perceptrons exist 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. 4 .

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. 4 , three layers are disclosed, but when counting the number of layers of an actual artificial neural network, since the count excludes the input layer, it can be regarded 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).

The deep neural network shown in FIG. 5 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. 5 is a diagram illustrating an example of a deep neural network.

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.

In DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, in FIG. 6, 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. 6). In this case, a weight is added for each connection in the connection process from one input node to the hidden layer, so a total of hХw weights must be considered. Since there are hХw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

6 is a diagram showing an example of a convolutional neural network.

The convolutional neural network of FIG. 6 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. As shown in , weighted sum and activation function calculations are performed for overlapping filters.

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. 6, a filter having a size of 3Х3 is applied to the 3Х3 area at the top left of the input layer, and an output value obtained by performing a weighted sum and an activation function operation for a corresponding node is stored in z22.

While scanning the input layer, the filter performs weighted sum and activation function calculations while moving horizontally and vertically at regular intervals, 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).

7 is a diagram showing an example of a filter operation in a convolutional neural network.

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.

Referring to FIG. 8, a recurrent neural network (RNN) assigns an element (x1(t), x2(t), ,..., xd(t)) of a line t on a data sequence to a fully connected neural network. During the input process, the immediately preceding time point t-1 is a structure in which a weighted sum and an activation function are applied by inputting the hidden vectors (z1(t1), z2(t1), ..., zH(t1)) together. 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.

8 shows an example of a neural network structure in which a cyclic loop exists.

Referring to FIG. 8 , the recurrent neural network operates in a sequence of predetermined views with respect to an input data sequence.

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

9 shows an example of an operating structure of a recurrent neural network.

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. 10 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 call-to-noise ratio, interference avoidance and rejection, and high network efficiency. Hologram beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

big data analytics

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

Large Intelligent Surface (LIS)

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

Terahertz (THz) wireless communication general

10 is a diagram showing an example of an electromagnetic spectrum.

THz wireless communication uses wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may mean terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or more. . 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. 11 is a diagram showing an example of a THz communication application.

As shown in FIG. 11, THz wireless communication scenarios can be classified into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle connections and backhaul/fronthaul connections. In micro networks, THz wireless communication is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading. It can be.

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

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. 12 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. 12, a doubler, tripler, or multiplier is applied to increase the frequency, and the radiation is emitted 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. 12 . 12, IF denotes an intermediate frequency, tripler and multipler denote a multiplier, PA denotes a power amplifier, LNA denotes a low noise amplifier, and PLL denotes a phase-locked circuit (Phase -Locked Loop).

13 is a diagram illustrating an example of a method for generating a THz signal based on an optical device, and FIG. 14 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. 13, 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. 13 , 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. 13, an optical coupler means 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-Travelling 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. 14, 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. 15 and 16 . 15 illustrates a structure of a transmitter based on a photoinc source, and FIG. 16 illustrates a structure of an optical modulator.

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

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

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

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

As discussed above, 6G mobile communication is expected to be used in various communication environments and transmission/reception devices. In order to guarantee optimal communication performance in various environments, the transmission/reception device itself needs to optimize a communication method suitable for the environment. However, the current transmission/reception method (or device), which is a fixed rule-based type, requires an expert's intervention or a change in the transmission/reception protocol to adapt to a new environment.

Therefore, in order for the communication method to adapt itself to the environment, the neural net-based transmission/reception method, which is a learning method, is preferred over the existing fixed logic method. In addition, the structure can be changed during neural network training, so that a more optimal state can be guaranteed in terms of performance and complexity. Therefore, the present specification provides a neural net-based automatic artificial intelligence transmission and reception apparatus and method that adapts itself to real situations with minimal feedback signaling to suit various devices and environments.

17 shows an example of a wireless communication system for an auto encoder transmission/reception method.

Auto Encoder transmission and reception methods that automatically design communication systems using artificial intelligence have been introduced in existing literature.

However, the conventional auto-encoder transmission/reception method (FIG. 17) uses a transmitter encoder neural network and a receiver decoder neural network instead of transmission and reception methods designed by experts for the transmitter and receiver respectively. While it takes a lot of effort and development period by experts to design an optimal communication method for a given environment, the existing auto-encoder transmission/reception method designs a transmission/reception method without expert effort.

That is, in the existing auto encoder transmission/reception method, the receiving end learns the two neural net weights of the auto encoder by the GD (Gradient Descent) method with the difference between the transmitted symbol and the received symbol, and then the two encoder neural nets and the decoder neural net are transmitted during the actual communication process. and used in the receiver respectively.

The GD method is a learning method used to optimize weight parameters in a neural network, and the Stochastic Gradient Descent (SGD) method is a learning method that finds an optimal value by turning it little by little as much as the mini-batch size.

An example of an update method of the GD method, that is, Stochastic Gradient Descent (SGD) may be as shown in Equation 1 below.

That is, the GD method uses a method of collecting and averaging error magnets as much as the batch size (N) and updating weights after differentiating them. s and r represent messages transmitted and received during a certain period (frame size). j_loss(s,r) is a loss suitable for the purpose such as MMSE (minimum mean-square-error), cross entropy, BLER (Block Error Rate), FER (Frame Error Rate), LLR (log-likelihood ratio) It can be set as a loss function. The loss function may refer to a function that informs the degree of error in the current weight, that is, an error.

The conventional automatic encoder transmission/reception method of FIG. 17 may have the following limitations.

When the method of FIG. 17 is used for a wireless communication environment, it is difficult to obtain optimal performance in a new environment if the learning environment is not considered or considered but learned in an average environment. For example, if the wireless interference environment becomes very poor due to the large number of other transmitting/receiving devices in the vicinity, or if the radio wave transmission environment changes greatly due to nearby buildings or regional conditions, the existing neural network configuration is not optimal and performance deteriorates. It can be.

Therefore, in order to apply an adaptive auto-encoder suitable for a changed environment, the following three items need to be considered in turn.

First, the weight of the transmitter encoder neural net among the two neural nets learned in the receiver must be fed back to the transmitter (or transmitter or transmitter) efficiently.

Second, existing autoencoders are directed toward designing the network structure by experts, but the network structure must be automatically optimized to adapt to the given communication environment.

Third, considering the first and second together, if the adaptive network structure is changed, the problem of all weights being learned from the beginning must be solved so that efficient weight feedback can be possible even in a structure change situation.

18 is a diagram showing an example of a wireless communication system for a transmission/reception method of an auto-encoder proposed in this specification.

The adaptive auto-encoder transmission/reception method of FIG. 18 is a method for adapting the auto-encoder transmission/reception method (FIG. 17) proposed in the existing method through weight feedback to suit a wireless communication channel condition.

The receiver (or receiver or receiver) learns the two neural net weights of the Otter encoder through the GD method as the difference between the transmitted symbol and the received symbol. As an example of the update method of the GD method, that is, stochastic gradient descent (SGD), is the same as Equation 1, which was previously considered, reference will be made to this.

That is, the GD method uses a method of collecting error magnets as much as the batch size (N), averaging, differentiating, and then updating weights. s and r indicate messages transmitted and received during a certain period (frame size), and j_loss (s, r) can be set to a loss function suitable for the purpose, such as MMSE, cross entropy, BLER, FER, and LLR.

And, the receiving end feeds back the weight of the transmitting end encoder neural net to the sending end.

The transmitting and receiving neural nets respectively use newly updated weights.

The Salpin adaptive auto-encoder transmission/reception method in FIG. 18 may have the following limitations.

When the receiving end transmits the updated weight information to the sending end neural net, the obtained weight value is directly fed back, not the difference from the previously transmitted information, so loss of the uplink control channel may be high.

In addition, the method proposed in FIG. 18 can adaptively update the neural nets of the transmitter and the receiver even when the communication situation changes, so that the performance can be guaranteed to some extent even in the changing situation. However, since the structure of the neural net used is not adaptively changed, there is a limit to adapting when a large change occurs. That is, reducing or increasing the number of nodes to change complexity is not considered, so there is a limit to optimizing performance.

19 is a diagram showing another example of a wireless communication system for the method of transmitting and receiving an auto encoder proposed in this specification.

That is, FIG. 19 relates to a wireless communication system for another automatic encoder transmission/reception method, that is, a wireless communication system for a limited feedback adaptive automatic auto encoder transmission/reception method.

In FIG. 18, in the Salpin adaptive auto encoder transmission/reception method, the difference between the neural net weight of the transmitter and previously transmitted information is transmitted in order to reduce the load of the uplink control channel.

As described above, the receiving end learns the two neural net weights of the auto encoder through the GD method as the difference between the transmitted symbol and the received symbol. An example of the update method of the GD method, that is, the SGD (Stochastic Gradient Descent) method, is the same as Equation 1 described in FIG. 17, so Equation 1 will be referred to.

Additionally, the difference from the previous update may be defined as in Equation 2 below.

를 송신단으로 피드백 한다.The receiving end, in particular, the neural net weight update part is the difference between the new weight and the previous weight of the sending end encoder neural net.

is fed back to the transmitter.

The transmitter NeurellNet updates the new weight by adding the newly received weight difference information to the previous weight information. The receiving end neural net immediately uses each updated weight.

In addition, the neural net weight update part changes the structure of the neural net and reflects the changed structural information to the neural net of the transmitter and the neural net of the receiver in order to further increase transmission/reception performance or reduce the complexity of neural net processing. The neural net of the transmitter is fed back with weights through an uplink control channel to the receiver, in particular, the neural net weight update part.

The automatic encoder transmission/reception method proposed in FIG. 19 may have the following limitations.

If the structure of the neural network is changed, it is generally necessary to newly find the weights from the beginning, so there is a problem in that the weight feedback required at that time increases. Therefore, even if the structure information is changed, a method for bringing the difference from the previous weight to a certain level is required so that the difference does not increase further.

20 is a diagram showing another example of a wireless communication system for a transmission/reception method of an auto-encoder proposed in this specification.

That is, FIG. 20 relates to a method for transmitting and receiving an effective limited feedback adaptive auto encoder.

The method proposed in FIG. 20 maintains constant uplink feedback information even if the neural network structure is changed in the auto encoder transmission/reception method of FIG. 19 and effectively updates the structure to reduce the amount of feedback information.

And, the receiving end learns the two neural net weights of the auto encoder using the GD method using the difference between the transmitted symbol and the received symbol.

를 송신단으로 피드백한다. 송신단 뉴렐넷은 이전 가중치 정보에 새로 받은 가중치 차이 정보를 더해 새로운 가중치로 업데이트 하고, 수신단 뉴럴넷은 각각 갱신된 가중치를 바로 사용한다. 더불어, 도 20의 뉴럴넷 가중치 갱신 파트는 송수신 성능을 더 높이거나 또는 뉴럴넷 프로세싱의 복잡도를 낮추기 위해 뉴럴넷의 구조를 변경시키고, 변경된 구조 정보를 송신단 뉴럴넷과 수신단 뉴럴넷에 반영시킨다. 그리고, 뉴럴넷 가중치 갱신 파트는 송신단 뉴럴넷으로 상향링크 제어 채널을 통해 가중치와 함께 피드백한다. The receiving end, in particular, the neural net weight update part is the difference between the new weight and the previous weight of the sending end encoder neural net.

is fed back to the transmitter. The transmitting NeuralNet updates the new weight by adding the newly received weight difference information to the previous weight information, and the receiving NeuralNet immediately uses each updated weight. In addition, the neural net weight update part of FIG. 20 changes the structure of the neural net to further increase transmission/reception performance or reduce the complexity of neural net processing, and reflects the changed structure information to the transmitter neural net and the receiver neural net. And, the neural net weight update part is fed back with the weight to the transmitter neural net through an uplink control channel.

The auto-encoder transmission/reception method proposed in FIG. 20 has the following differences from other auto-encoder transmission/reception methods discussed above.

When a new node is added or removed from a specific layer of a neural network at the sending end to adjust performance, the output of the new network that is increased or decreased using the net2net operation (add/subtract) is the result of the network of the previous structure. Make it the same or similar to the processing result so that the previous weights can be reused. As a result, the newly obtained weight does not have a large change compared to the previous weight. Technically, it is a method that introduces the concept of transfer learning.

Since Net2net operation is used, feedback is possible by applying the recycle weight feedback scheme as shown in Figure 21 even if the weights are not reset despite structural changes. Therefore, the amount of weights to be fed back does not increase even at the time of structure change. In addition, when the structure change information is transmitted to the transmitter (Tx), the transmitter itself performs the net2net operation according to the situation, so additional signaling for performing the net2net operation is not required.

21 is a diagram illustrating an example of a recycling weight feedback method as shown in FIG. 21 proposed in this specification.

The Net2net add operation makes the previously processed result of the network with a small number of nodes identical to the output result of the network to which nodes are added so that the previous weight can be reused so that the newly obtained weight has high correlation with the previous weight. say

In addition, the net2net subtract operation, when removing nodes of a specific layer of the transmitter neural net for performance optimization or complexity adjustment, makes the output value as much as possible the same as the output value when the previous node exists even when the number of nodes is reduced.

In addition, since the node is removed, it may not be possible to completely match the same, but the change in the previous weight values and the new weight values is made as small as possible so that the feedback change due to the structural change due to the removal of the node is not large. Here, basic methods (linear), advanced methods (using NN), simple methods, and the like can be used.

22 is a diagram showing an example of a NettoNet addition operation proposed in this specification.

In FIG. 22, the added node (h[3], 2210) determines the initial values of the input weight and the output weight using an existing node (h[2], 2220) nearby. The input weights of the additional node (h[3]) are the same as those of the existing node (h[2]) (c, d), and the output weight is divided by half the weight of the existing node (h[2]) (f /2) It is used by replacing with the weight of the existing node and the new node. That is, if this is expressed as an equation, it is as Equation 3 and Equation 4 below.

That is, the output weights are f(h[2]==>y) divided in half and used as f/2(h[2]==>y) and f/2(h[3]==>y), respectively. do.

Next, we look at the net2net subtract operation. The net2net subtract operation has a basic method, a simple method, and an advanced method, and we will look at them in turn.

basic method

A more detailed description of the basic method of the net2net subtract operation will be described later, and hereinafter, a brief description will be given focusing on the concept.

That is, the basic method applies the average of the input weight of h[3], the node to be removed, and the input weight of h[2], a random neighboring node, to h[2]. That is, c ==> (c + c')/2, d ==> (d + d')/2, and the output weight is similarly set to f ==> f + f'.

simple way

The simple method uses the previous weights as they are after node removal. From the performance point of view, the optimum should be used after resetting, but by using the concept of transfer learning, learning is performed while maintaining the previous weights to minimize the time spent on learning and to minimize the change in weights.

advanced method

Advanced methods use neural net methods to increase accuracy.

Before the node to be removed (h[3]) is removed, the input value to the y node of the next layer is as shown in Equation 5 below.

If the last node (h[3]) is removed, H' ==> H - h[3], so input and output weights (c, d, f) of random neighboring nodes (h[2]) and Adjust so that H' = H as much as possible.

In order to increase accuracy, an advanced method using a neural net takes the outputs (x[1], x[2]) of the previous (input) layer as input, and the output is the output value (y) of the next (output) layer. Create a small neural net to find c, d, and f. For this method, the outputs of the two stages are calculated and stored after the previous training is completed. In particular, when there is a request to change the node structure, the request is processed by the transmitting end. Since the output is limited to the neural net output of the transmitting end, there is an advantage that the transmitting end itself can calculate it.

Next, we look at the basic method of Net2Net subtraction operation in more detail.

When input is partially shared in common

If node N is removed, the node with the largest index among the nodes that share input with node N is compensated.

In the compensation method for re-initialization, the output weight for the node to be compensated is added by the output weight of the node N, and when the output weights of the second and third nodes are expressed as f and f', after removing the third node f becomes f + f'.

The input weight of the node to be compensated is either (1) left unchanged (the effect of the removed node being refined is ignored and the node to be compensated thinks that its optimal value is the same as that of the removed node) or (2 ) to the weighted average of the input weights of the removed node and the compensated node.

That is, when the input weights of the second and third nodes are generally expressed as c, d, and c', d', after removing number 3, c is c*|c|/(|c|+|c '|) + c'*|c'|/(|c|+|c|), and d is d*|d|/(|d|+|d'|) + d'*|d'| /(|d|+|d'|) for plausible re-initialization.

Another method is to give weights by 1/2 so that c is c/2 + c'/2 and d is d/2 + d'/2.

In compensation method 1, if there are K nodes to be compensated, the output weights of the nodes to be removed are attenuated by 1/K and added to the output weights of the K nodes, and the input weights of the nodes to be compensated are as described above (1). or (2) using one of the methods.

Finally, when choosing which node to remove, we apply a rule that preferentially removes the node with the smallest Sum{|input weight|} * Sum{|output weight|} to minimize the impact of node removal. .

Here, |x| represents the norm of x. An absolute value (L1-norm) is appropriate, but a generalized norm such as L2-norm can be used.

When input is partially shared

This is an unusual situation, but if it does occur, if node N is removed, the node with the largest index among the nodes that share the most input with node N is compensated. In the case of partial sharing, the input weight of non-shared nodes can be regarded as '0' and processed similarly to the case of common sharing above.

23 is a flowchart illustrating an example of a method for transmitting and receiving signals in a wireless communication system using an auto-encoder proposed in this specification.

First, the receiving end receives the first weight of the transmitting end encoder neural network from the sending end (S2310).

Here, the receiving end may be expressed as a receiving device, a terminal, a receiving unit, and the like.

Then, the receiving end learns the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method (S2320).

Here, the learning based on the gradient descent method may be performed using a difference between a transmission symbol of a transmission signal and a reception symbol of a reception signal for the transmission signal.

Then, the receiving end transmits updated weight information updated for the first weight based on the learning to the transmitting end (S2330).

The update weight information may be a differential value between a previous weight value of the first weight and a new weight value of the first weight.

And, the receiving end receives the signal to which the update weight information is applied from the transmitting end (S2340).

Additionally, the receiving end may apply the updated weight information to the receiving end encoder neural network.

Additionally, the receiving end may transmit neural network structure information on at least one of a structure of the transmitting end encoder neural net and a structure of the receiving end encoder neural net to the sending end. At this time, the receiving end may transmit the neural network structure information together with the update weight information to the transmitting end.

The neural network structure information may be transmitted to the transmitter when at least one of the structure of the transmitter encoder neural net and the structure of the receiver encoder neural net is changed.

In addition, the structure of the transmitter encoder neural net or the structure of the receiver encoder neural net may be changed when a new node is added to a specific layer of the neural network or an arbitrary node is removed.

Devices used in wireless communication systems

Although not limited thereto, various proposals of the present invention described above may be applied to various fields requiring wireless communication/connection (eg, 5G) between devices.

Hereinafter, it will be exemplified in more detail with reference to the drawings. In the following drawings/description, the same reference numerals may represent the same or corresponding hardware blocks, software blocks or functional blocks unless otherwise specified.

24 illustrates a communication system applied to the present invention.

Referring to FIG. 24, a communication system 1 applied to the present invention 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 may be performed between the wireless devices 100a to 100f/base station 200-base station 200/wireless devices 100a to 100f. Here, wireless communication/connection may be performed through various radio access technologies (eg, 5G NR) for uplink/downlink communication 150a and sidelink communication 150b (or D2D communication). Through the wireless communication/connection 150a and 150b, the wireless device and the base station/wireless device may transmit/receive radio signals to each other. For example, the wireless communication/connection 150a and 150b may transmit/receive signals through various physical channels based on all/partial processes of FIG. 1 . To this end, based on the various proposals of the present invention, various configuration information setting processes for transmitting / receiving radio signals, various signal processing processes (eg, channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.) At least a part of a resource allocation process may be performed.

25 illustrates a wireless device applicable to the present invention.

Referring to FIG. 25 , 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 and the second wireless device 200} refer to {the wireless device 100x and the base station 200} of FIG. 24 and/or {the wireless device 100x and 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 functions, procedures and/or methods described/suggested above. 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 store software code including instructions for performing procedures and/or methods described/suggested above. . 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 the present invention, 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. The processor 202 controls the memory 204 and/or the transceiver 206 and may be configured to implement the functions, procedures and/or methods described/suggested above. 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 may store software code including instructions for performing procedures and/or methods described/suggested above. . 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 the present invention, 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) according to functions, procedures, proposals and/or methods disclosed herein. One or more processors 102, 202 may generate messages, control information, data or information according to functions, procedures, suggestions and/or methods 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 generate PDUs, SDUs according to functions, procedures, proposals and/or methods disclosed herein. , messages, control information, data or information can be obtained.

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 functions, procedures, proposals and/or methods 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 functions, procedures, suggestions and/or methods disclosed herein may be included in one or more processors (102, 202) or stored in one or more memories (104, 204) and may be stored in one or more processors (102, 202). 202). The functions, procedures, suggestions and/or methods disclosed herein 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 of the transceivers 106, 206 may receive user data, control information, radio signals/channels, etc. referred to in functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. 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 may perform functions, procedures disclosed herein. , It can be set to transmit and receive user data, control information, radio signals / channels, etc. mentioned in proposals, methods, and / or operational flowcharts. 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.

26 illustrates a signal processing circuit for a transmission signal.

Referring to FIG. 26 , 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. 26 may be performed by the processors 102 and 202 and/or the transceivers 106 and 206 of FIG. 26 . The hardware elements of FIG. 26 may be implemented in processors 102 and 202 and/or transceivers 106 and 206 of FIG. 25 . For example, blocks 1010-1060 may be implemented in the processors 102 and 202 of FIG. 25 . Also, blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 25 , and block 1060 may be implemented in the transceivers 106 and 206 of FIG. 25 .

The codeword may be converted into a radio signal through the signal processing circuit 1000 of FIG. 26 . 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). The radio signal may be transmitted through various physical channels (eg, PUSCH, PDSCH) of FIG. 1 .

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. 26 . For example, wireless devices (eg, 100 and 200 of FIG. 25 ) 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.

27 shows another example of a wireless device applied to the present invention. A wireless device may be implemented in various forms according to use-case/service.

Referring to FIG. 27, wireless devices 100 and 200 correspond to the wireless devices 100 and 200 of FIG. 25, 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. 25 and/or one or more memories 104, 204. For example, transceiver(s) 114 may include one or more transceivers 106, 206 of FIG. 25 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. 24, 100a), a vehicle (Fig. 24, 100b-1, 100b-2), an XR device (Fig. 24, 100c), a mobile device (Fig. 24, 100d), a home appliance. (FIG. 24, 100e), IoT device (FIG. 24, 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. 24, 400), a base station (Fig. 24, 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. 27 , various elements, components, units/units, and/or modules in the wireless devices 100 and 200 may all be interconnected through a wired interface, or at least some of them may be wirelessly connected 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. 27 will be described in more detail with reference to drawings.

28 illustrates a portable device applied to the present invention. 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. 28, a 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. 27 .

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.

29 illustrates a vehicle or autonomous vehicle to which the present invention is applied. Vehicles or autonomous vehicles may be implemented as mobile robots, vehicles, trains, manned/unmanned aerial vehicles (AVs), ships, and the like.

Referring to FIG. 29 , 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. 27 .

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.

30 illustrates a vehicle to which the present invention is applied. A vehicle may be implemented as a means of transportation, a train, an air vehicle, a ship, and the like.

Referring to FIG. 30 , 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 respectively correspond to blocks 110 to 130/140 of FIG. 27 .

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 abnormality to related agencies through the communication unit 110 .

31 illustrates an XR device applied to the present invention. 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. 31 , 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 respectively correspond to blocks 110 to 130/140 of FIG. 27 .

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 transmitted 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.

32 illustrates a robot applied to the present invention. Robots may be classified into industrial, medical, household, military, and the like depending on the purpose or field of use.

Referring to FIG. 32 , 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 respectively correspond to blocks 110 to 130/140 of FIG. 27 .

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.

33 illustrates an AI device applied to the present invention. 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. 33, 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 to 130/140a to 140d respectively correspond to blocks 110 to 130/140 of FIG. 27 .

The communication unit 110 transmits wired and wireless signals (eg, sensor information, user input, and learning) to external devices such as other AI devices (eg, FIG. 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 ( 24, 400) can be transmitted to an external device. 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. 24, 400). The learning processor unit 140c may process information received from an external device through the communication unit 110 and/or information stored in the memory unit 130 . In addition, the output value of the learning processor unit 140c may be transmitted to an external device through the communication unit 110 and/or stored in the memory unit 130.

The embodiments described above are those in which elements and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, it is also possible to configure an embodiment of the present invention by combining some components and/or features. The order of operations described in the embodiments of the present invention may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that claims that do not have an explicit citation relationship in the claims can be combined to form an embodiment or can be included as new claims by amendment after filing.

An embodiment according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, one embodiment of the present invention provides one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), FPGAs ( field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software code can be stored in memory and run by a processor. The memory may be located inside or outside the processor and exchange data with the processor by various means known in the art.

It is apparent to those skilled in the art that the present invention can be embodied in other specific forms without departing from the essential characteristics of the present invention. Accordingly, the foregoing detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Although the present invention has been described focusing on examples applied to 3GPP LTE/LTE-A and 5G systems, it is possible to apply to various wireless communication systems other than 3GPP LTE/LTE-A and 5G systems.

Claims (9)

In a method for transmitting and receiving a signal in a wireless communication system using an auto encoder, the method performed by a receiving end,
Receiving a first weight of a transmitter encoder neural network from a transmitter;
learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method;
transmitting update weight information updated for the first weight based on the learning to the transmitter; and
and receiving, from the transmitter, a signal to which the updated weight information is applied. According to claim 1,
The step of learning based on the gradient descent method,
A method characterized in that learning is performed using a difference between a transmission symbol of a transmission signal and a reception symbol of a reception signal for the transmission signal. According to claim 1,
The method further comprising applying the updated weight information to the receiving end encoder neural network. According to claim 1,
Wherein the update weight information is a difference value between a previous weight value of the first weight and a new weight value of the first weight. According to claim 1,
The method further comprising transmitting neural network structure information on at least one of a structure of the transmitter encoder neural net and a structure of the receiver encoder neural net to the transmitter. According to claim 5,
The method characterized in that the neural network structure information is transmitted to the transmitter together with the update weight information. According to claim 6,
The method characterized in that the neural network structure information is transmitted to the transmitting end when at least one of the structure of the transmitting end encoder neural net and the structure of the receiving end encoder neural net is changed. According to claim 7,
The structure of the transmitter encoder neural net or the structure of the receiver encoder neural net is changed when a new node is added to a specific layer of the neural network or an arbitrary node is removed. In the receiving end for transmitting and receiving signals in a wireless communication system using an auto encoder,
a transmitter for transmitting a radio signal;
a receiver for receiving a radio signal;
at least one processor; and
at least one computer memory operably connectable to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations;
These actions are
Receiving a first weight of a transmitter encoder neural network from a transmitter;
learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method;
transmitting update weight information updated for the first weight based on the learning to the transmitter; and
and receiving, from the transmitter, a signal to which the updated weight information is applied.

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