WO2022270651A1 - 오토 인코더를 이용하는 무선 통신 시스템에서 신호를 송수신하기 위한 방법 및 이를 위한 장치 - Google Patents
오토 인코더를 이용하는 무선 통신 시스템에서 신호를 송수신하기 위한 방법 및 이를 위한 장치 Download PDFInfo
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Definitions
- 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.
- 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.
- CDMA Code Division Multiple Access
- FDMA Frequency Division Multiple Access
- TDMA Time Division Multiple Access
- SDMA Space Division Multiple Access
- OFDMA Orthogonal Frequency Division Multiple Access
- SC-FDMA Single Carrier Frequency Division Multiple Access
- IDMA Interleave Division Multiple Access
- An object of the present specification is to provide a method for transmitting and receiving a signal in a wireless communication system using an auto-encoder and an apparatus therefor.
- an object of the present specification is to provide a method and apparatus for operating multiple auto-encoders for quantized multiple complex channels in a wireless communication system using auto-encoders.
- an object of the present specification is to provide a method and apparatus for operating multiple autoencoders for multiple complex channels quantized for each channel vector constituting a channel matrix of a complex channel in a wireless communication system using an autoencoder.
- an object of the present specification is to provide a method and apparatus for operating multiple auto-encoders for multiple complex channels quantized for each channel matrix of a complex channel in a wireless communication system using an auto-encoder.
- the present specification provides a method and apparatus for adaptively changing operation between a transmitting and receiving end for a time-varying channel environment using multiple auto-encoders for quantized multiple complex channels in a wireless communication system using auto-encoders. has a purpose
- the present specification provides a method for transmitting and receiving a signal in a wireless communication system using an auto encoder and an apparatus therefor.
- the present specification provides a method for a transmitting end to transmit data in a wireless communication system using an auto encoder, to a receiving end, (i) channel estimation and (ii) a specific auto for transmission of the data. Transmitting a reference signal for calculating a quantized channel information (QCI) index related to the decision of the encoder; Receiving feedback including the QCI index from the receiving end; and transmitting, to the receiving end, the data using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence information, wherein the correspondence information includes a complex channel Information related to a correspondence relationship between a plurality of quantized channels configured by quantizing space and a plurality of auto encoders pre-trained based on the plurality of quantization channels, is quantized to satisfy a specific condition based on an error between an actual channel related to any one quantization channel among the plurality of quantization channels and the one quantization channel.
- QCI quantized channel information
- the present specification is characterized in that the specific condition is a condition in which a value of cross correlation determined based on the error between the real channel and the one of the quantization channels is equal to or less than a preset value.
- the product (N t N r ) of the number of antennas of the transmitter (N t) and the number of antennas of the receiver (N r) and (ii) the error between the specific channel and the one quantization channel Based on this, the number of quantization bits (q) for representing the plurality of quantization channels is determined, and the number of the plurality of quantization channels is 2 q .
- the present specification further comprises pre-training the auto-encoders corresponding to each of the plurality of quantization channels of the 2 q number, wherein the plurality of auto-encoders are composed of the pre-trained auto-encoders. can do.
- feedback including a QCI index is received at a regular period, and the predetermined period is determined based on a time for which a specific quantization channel corresponding to the specific auto encoder maintains a state satisfying the specific condition.
- the predetermined period is determined based on a time for which a specific quantization channel corresponding to the specific auto encoder maintains a state satisfying the specific condition.
- the present specification may be characterized in that the time during which the specific quantization channel satisfies the specific condition is maintained based on the cross-correlation coefficient and the moving speed of the receiving end.
- the present specification includes transmitting, to the receiving end, a signal for setting the predetermined period, wherein the predetermined period is a value smaller than the time for which the specific quantization channel satisfies the specific condition is maintained. It can be characterized as being set.
- the plurality of quantization channels may be configured for at least one channel vector constituting a channel matrix.
- the correspondence relationship information is information related to a correspondence relationship between a plurality of quantization channel combinations and the plurality of auto encoders, which are combined based on the plurality of quantization channels configured for the at least one channel vector, respectively, and , wherein the plurality of quantization channel combinations and the plurality of auto encoders are mapped to each other, and the quantization channel combination includes at least one quantization channel respectively corresponding to the at least one channel vector.
- the QCI index represents one quantization channel combination among the plurality of quantization channel combinations
- one auto encoder mapped to the one quantization channel combination based on the correspondence relationship information is the specific auto encoder. It may be characterized as being determined by an encoder.
- the correspondence relationship information is information related to a correspondence relationship between the plurality of quantization channels and the plurality of auto encoders respectively configured for the at least one channel vector, and each of the at least one channel vector It may be characterized in that the configured plurality of quantization channels and the plurality of auto encoders are mapped to each other.
- the QCI index represents one quantization channel among the plurality of quantization channels for each of the at least one channel vector, and based on the correspondence relationship information, each of the at least one channel vector , one auto-encoder mapped to the one quantization channel is determined, and one auto-encoder determined for each of the at least one channel vector is the specific auto-encoder.
- the plurality of quantization channels are configured with respect to a channel matrix
- the correspondence relationship information is information related to a correspondence relationship between the plurality of quantization channels configured with respect to the channel matrix and the plurality of auto encoders, It may be characterized in that the plurality of quantization channels configured with respect to the channel matrix and the plurality of auto encoders are mapped to each other.
- the QCI index represents one quantization channel among the plurality of quantization channels configured for the channel matrix, and one auto encoder mapped to the one quantization channel based on the correspondence relation information It may be characterized in that it is determined by the specific auto-encoder.
- each of the plurality of auto-encoders is mapped to one of the plurality of quantization channels
- each of the plurality of auto-encoders is mapped to the plurality of quantization channels. It may be characterized in that it is mapped to at least two or more quantization channels among quantization channels of .
- the present specification provides a transmitter for transmitting and receiving signals in a wireless communication system using an auto encoder, comprising: 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 for performing operations when executed by the at least one processor, the operations comprising: , (i) channel estimation and (ii) transmitting a reference signal for calculating a quantized channel information (QCI) index related to determination of a specific auto-encoder for transmission of the data; Receiving feedback including the QCI index from the receiving end; and transmitting, to the receiving end, the data using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence information, wherein the correspondence information includes a complex channel Information related to a correspondence relationship between a plurality of quantized channels configured by quantizing space and a plurality of auto encoders pre-trained based on the plurality of quantization channels, is quant
- the present specification provides a method for a receiving end to receive data in a wireless communication system using an auto encoder, from a transmitting end, (i) channel estimation and (ii) a specific auto encoder for receiving the data.
- QCI quantized channel information
- a transmitter for transmitting a radio signal
- a receiver for receiving a radio signal
- at least one processor for receiving a radio signal
- at least one processor operably connectable to the at least one processor and storing instructions for performing operations when executed by the at least one processor, the operations comprising: , (i) channel estimation and (ii) receiving a reference signal for calculating a QCI (Quantized Channel Information) index related to determination of a specific auto-encoder for receiving the data; Transmitting feedback including the QCI index to the transmitter; and receiving the data from the transmitter using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence information, wherein the correspondence information includes a complex channel Information related to a correspondence relationship between a plurality of quantized channels configured by quantizing space and a plurality of auto encoders pre-trained based on the plurality of quantization channels,
- a non-transitory computer readable medium storing one or more instructions
- one or more instructions executable by one or more processors are transmitted by a transmitting end to a receiving end, (i) channel estimation and (ii) to transmit a reference signal for calculating a QCI (Quantized Channel Information) index related to the determination of a specific auto-encoder for transmission of the data, and from the receiving end, feedback including the QCI index (feedback), and transmit the data to the receiving end using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence relationship information
- the information is information related to a correspondence relationship between a plurality of quantized channels configured by quantizing a complex channel space and a plurality of autoencoders pre-trained based on the plurality of quantization channels.
- the plurality of quantization channels are quantized to satisfy a specific condition based on an error between an actual channel related to any one quantization channel among the plurality of quantization channels
- the one or more processors in an apparatus including one or more memories and one or more processors functionally connected to the one or more memories, the one or more processors, as a receiving end, (i) channel estimation and (ii) to transmit a reference signal for calculating a QCI (Quantized Channel Information) index related to the determination of a specific auto-encoder for transmission of the data, and feedback including the QCI index from the receiving end ), and transmit the data to the receiving end using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence information, wherein the correspondence information Information related to a correspondence relationship between a plurality of quantized channels configured by quantizing a complex channel space and a plurality of auto encoders pre-trained based on the plurality of quantization channels,
- the quantization channel is characterized in that it is quantized to satisfy a specific condition based on an error between an actual channel related to any one quantization channel among the plurality of quantization channels and the one quantization channel.
- the present specification has the effect of transmitting and receiving signals in a wireless communication system using an auto-encoder.
- the present specification has an effect of operating multiple autoencoders for multiple complex channels quantized for each channel vector constituting a channel matrix of a complex channel in a wireless communication system using an autoencoder.
- the present specification has an effect of operating multiple auto-encoders for multiple complex channels quantized for each channel matrix of a complex channel in a wireless communication system using an auto-encoder.
- an operation between transmitting and receiving ends can be adaptively changed for a time-varying channel environment.
- the present specification has an effect of maintaining performance of a wireless communication system using an auto-encoder even when a channel environment changes in a wireless communication system using an auto-encoder.
- 1 illustrates physical channels and typical signal transmission used in a 3GPP system.
- FIG. 2 is a diagram showing an example of a communication structure that can be provided in a 6G system.
- FIG. 3 is a diagram showing an example of a perceptron structure.
- FIG. 4 is a diagram showing an example of a multilayer perceptron structure.
- 5 is a diagram showing an example of a deep neural network.
- FIG. 6 is a diagram showing an example of a convolutional neural network.
- FIG. 7 is a diagram showing an example of a filter operation in a convolutional neural network.
- FIG. 8 shows an example of a neural network structure in which a cyclic loop exists.
- FIG. 9 shows an example of an operating structure of a recurrent neural network.
- FIG. 10 is a diagram showing an example of a method in which a deep learning-based AI algorithm based on offline learning is applied to a communication environment.
- FIG. 10 is a diagram showing an example of a method in which a deep learning-based AI algorithm based on offline learning is applied to a communication environment.
- FIG. 11 is a diagram showing another example of a method in which a deep learning-based AI algorithm based on offline learning is applied to a communication environment.
- FIG. 12 is a diagram illustrating examples of quantized complex MIMO channel space.
- FIG. 31 is a diagram illustrating examples of quantized complex MIMO channel space.
- FIG. 13 is a diagram illustrating an example of a communication system to which an auto-encoder is applied.
- FIG. 14 is a diagram illustrating an example of a quantized complex channel space.
- 15 is a diagram illustrating conditions for maintaining target performance according to a moving speed of a terminal.
- 16 is a diagram illustrating another example of a communication system to which an auto-encoder is applied.
- 17 is a diagram illustrating an example of a method of quantizing a complex channel space by individually quantizing column vectors of a channel matrix.
- 19 is a diagram illustrating another example of a method of constructing a correspondence relationship between a quantization channel configured by individually quantizing a column vector of a channel matrix and an auto encoder.
- 20 is a diagram illustrating an example of a method of quantizing a complex channel space based on a stacked channel formed by stacking column vectors of a channel matrix.
- 21 is a diagram illustrating an example of a method of constructing a correspondence relationship between a stacked quantization channel configured by stacking column vectors of a channel matrix and an auto encoder.
- 22 is a flowchart illustrating an example of an operation between a transmitter/receiver in a multi-auto encoder system operating in an FDD system.
- 23 is a flowchart illustrating an example of an operation between a transmitter/receiver in a multi-auto encoder system operating in a TDD system.
- 24 is a flowchart illustrating an example of a method for transmitting data in a wireless communication system using an auto-encoder proposed in this specification.
- 25 illustrates a communication system applied to the present invention.
- 26 illustrates a wireless device applicable to the present invention.
- 29 illustrates a portable device applied to the present invention.
- FIG. 30 illustrates a vehicle or autonomous vehicle to which the present invention is applied.
- 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).
- GSM Global System for Mobile communications
- GPRS General Packet Radio Service
- EDGE Enhanced Data Rates for GSM Evolution
- 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
- LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE.
- 3GPP NR New Radio or New Radio Access Technology
- 3GPP 6G may be an evolved version of 3GPP NR.
- LTE refers to technology after 3GPP TS 36.xxx Release 8.
- LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A
- 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.
- RRC Radio Resource Control
- RRC Radio Resource Control
- 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.
- 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.
- PSS primary synchronization signal
- SSS secondary synchronization signal
- PBCH physical broadcast channel
- DL RS downlink reference signal
- the UE 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).
- PDCCH Physical Downlink Control Channel
- PDSCH Physical Downlink Shared Channel
- the terminal may perform a random access procedure (RACH) for the base station (S13 to S16).
- RACH random access procedure
- 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
- PRACH physical random access channel
- RAR Random Access Channel
- a contention resolution procedure may be additionally performed (S16).
- 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.
- the terminal may receive downlink control information (DCI) through the PDCCH.
- DCI downlink control information
- 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.
- 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.
- 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.
- PDSCH Physical Downlink Shared Channel
- 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.
- QPSK Quadrature Phase Shift Keying
- QAM 16 Quadrature Amplitude Modulation
- 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.
- DMRS demodulation reference signal
- the PDCCH carries downlink control information (DCI) and a QPSK modulation method or the like is applied.
- DCI downlink control information
- One PDCCH is composed of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) according to an Aggregation Level (AL).
- CCE is composed of 6 REGs (Resource Element Groups).
- 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.
- 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.
- PUSCH Physical Uplink Shared Channel
- 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 , DFT-s-OFDM (Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing) is transmitted based on a waveform.
- DFT-s-OFDM Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing
- the UE transmits the PUSCH by applying transform precoding.
- the terminal 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 can be performed based on codebook or non-codebook.
- 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 (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 can have key factors such as access network congestion and enhanced data security.
- eMBB Enhanced mobile broadband
- URLLC Ultra-reliable low latency communications
- mMTC massive machine-type communication
- AI integrated communication Tactile internet
- High throughput High network capacity
- High energy efficiency High energy efficiency
- Low backhaul Low backhaul and It can have key factors such as access network congestion and enhanced data security.
- FIG. 2 is a diagram showing an example of a communication structure that can be provided in a 6G system.
- 6G systems are expected to have 50 times higher simultaneous radiocommunication connectivity than 5G radiocommunication systems.
- URLLC a key feature of 5G, will become even more important in 6G communications by providing end-to-end latency of less than 1 ms.
- the 6G system will have much better volume spectral efficiency as opposed to the frequently used area spectral efficiency.
- 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices will not need to be charged separately in 6G systems.
- New network characteristics in 6G may be as follows.
- 6G Satellites integrated network
- AI can be applied at each step of the communication process (or each step of signal processing described below).
- 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 networks will be another important feature of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.
- 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.
- High-precision localization (or location-based service) through communication is one of the features of 6G wireless communication systems.
- radar systems will be integrated with 6G networks.
- AI 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.
- 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-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.
- a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms.
- 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.
- DL downlink
- 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.
- 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.
- a reverse direction ie, from the output layer to the input layer
- 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.
- DNN deep neural networks
- CNN convolutional deep neural networks
- RNN recurrent Boltzmann Machine
- An artificial neural network is an example of connecting several perceptrons.
- 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.
- an input value or an output value is referred to as a node.
- 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
- all the layers located between the input layer and the output layer are called hidden layers.
- 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.
- CNN neural network
- RNN multi-layer perceptrons
- DNN deep neural network
- 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.
- 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.
- the correlation characteristic may mean a joint probability of input and output.
- 5 is a diagram illustrating an example of a deep neural network.
- nodes located inside one layer are arranged in a one-dimensional vertical direction.
- the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically (convolutional neural network structure of FIG. 6).
- a weight is added for each connection in the connection process from one input node to the hidden layer, so a total of h ⁇ w weights must be considered. Since there are h ⁇ w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.
- FIG. 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.
- a filter having a size of 3 ⁇ 3 is applied to the 3 ⁇ 3 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.
- the filter 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.
- a neural network having a plurality of convolutional layers is referred to as a deep convolutional neural network (DCNN).
- FIG. 7 is a diagram showing an example of a filter operation in a convolutional neural network.
- 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.
- a recurrent neural network assigns an element (x1(t), x2(t), ,..., xd(t)) of a line t on a data sequence to a fully connected neural network.
- 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.
- 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.
- FIG. 9 shows an example of an operating structure of a recurrent neural network.
- a deep recurrent neural network a recurrent neural network
- Recurrent neural networks are designed to be usefully applied to sequence data (eg, natural language processing).
- Deep Q-Network 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.
- RBM Restricted Boltzmann Machine
- DNN deep belief networks
- Deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.
- AI algorithms based on deep learning that can be applied to the communication environment can be divided into offline learning and online learning.
- offline learning it may be assumed that learning is completed before the communication system is activated, and the deep learning AI algorithm operates in the communication environment based on the learned result.
- online learning learning is performed even after the communication system is activated, and the deep learning AI algorithm can be assumed to operate by adaptively reflecting updated learning results to the activated communication system.
- the performance of a deep learning-based AI algorithm may have performance close to optimal.
- the communication environment channel environment
- the complex channel space constituted by the multi-antenna system of the transceiver/receive end may be expressed as a space represented by a sphere.
- a specific channel vector (Vector) H_trained may indicate a specific direction on a complex channel space.
- H_trained means a specific communication environment in which offline learning is performed.
- the deep learning-based AI algorithm may have performance close to optimal.
- the AI algorithm may be an autoencoder or an AI detector.
- the communication system can achieve optimal performance. Referring to FIG. 10 (b), it can be confirmed that the performance of the AI algorithm for which offline learning has been completed for the H_trained channel is close to the optimal performance.
- FIG. 11 is a diagram showing another example of a method in which a deep learning-based AI algorithm based on offline learning is applied to a communication environment. More specifically, FIG. 11 relates to a case where an actual communication (channel) environment and a learned communication (channel) environment are not at the same level.
- the distance between the direction of the vector H_trained corresponding to the communication (channel) environment in which the deep learning-based AI algorithm has completed learning and the direction of the vector H_real corresponding to the actual communication (channel) environment in the complex space increases.
- the performance of the communication system is reduced.
- the direction of the vector H_trained corresponding to the communication (channel) environment in which the deep learning-based AI algorithm has completed learning by channel change and the direction of the vector H_real corresponding to the actual communication (channel) environment It can be seen that the distance in the complex space of is separated by ⁇ H.
- FIG. 11(b) shows the performance change according to ⁇ 2 that determines the amount of change ⁇ H between H_trained and H_real.
- the communication system can achieve optimal performance.
- the distance between the learned channel and the real channel in the channel complex space increases, the BER performance decreases.
- a method for adaptively changing the operation of the transceiver for the time-varying channel of the communication system (e.g., B5G, 6G, B6G, etc.) has been proposed in advance.
- the method for adaptively changing the operation of the transmitting and receiving end must be defined in advance as an agreement of the transmitting and receiving end in a form that can allow the change of operation, and the operation change can be indicated between the receiving ends. this should be defined.
- channel state information (CSI) for exchanging information on a channel between transmitting and receiving ends is a precoding matrix indicator (PMI), CQI (channel quality information), RI (rank indicator), LI (layer indicator), and the like.
- the receiving end performs CSI feedback, and the transmitting end can recognize the channel to the receiving end through the CSI feedback. Therefore, the channel between the transmitting and receiving ends is a concept quantized by the number of PMI, CQI, RI, and LI bits constituting CSI, and free A precoder may be used. That is, the channel between the transmitting and receiving ends is quantized into 2 ⁇ (# of PMI, CQI, RI, LR bits) and used for transmission of information on the channel of the transmitting and receiving ends.
- Quantization of a complex MIMO channel formed by a multi-antenna system of a transmitting/receiving end may mean quantizing a unit sphere space representing a complex channel in various ways.
- various methods such as random vector quantization (RVQ), grassmannian line packing (GLP), centroidal Voronoi tessellation (CVT), and cube split codebook may be used as a method of quantizing the complex channel space.
- FIG. 12 is a diagram illustrating examples of quantized complex MIMO channel space.
- 12(a) shows a complex MIMO channel space quantized based on a cube division codebook
- FIG. 12(b) shows a complex MIMO channel space quantized based on CVT.
- the quantized complex channel space is composed of a plurality of quantized channels, and consecutive channels included in a region covered by the quantized channels may be mapped to the quantized region.
- This specification proposes a method of defining, learning, and operating an auto encoder of a transmitting/receiving end for each quantized channel in a communication system to which limited feedback is applied.
- a quantized channel may be expressed in various ways within a similarly interpreted range, such as a quantization channel.
- FIG. 13 is a diagram illustrating an example of a communication system to which an auto-encoder is applied.
- the transmitter 1310 generates encoded data x by inputting data s to be transmitted to the transmitter neural network (Tx NN). Thereafter, the transmitter 1310 transmits the generated encoded data x to the receiver 1320 through a channel. Thereafter, the receiving end 1320 inputs the signal y received through the channel to the receiving end neural network (Rx NN), receives a probability vector p as an output, and decodes data s.
- the transmitter neural network, the channel, and the receiver neural network may be composed of one neural network as a whole, and may be trained as one neural network.
- the transmitter neural network is a transmitter autoencoder (Tx Autoencoder: Tx AE)
- the receiver neural network is a receiver autoencoder (Rx Autoencoder: Rx AE) ) and can be called separately.
- the entire neural network may be collectively referred to as an autoencoder (AE).
- the auto encoder can be learned for one fixed channel. In this case, when the difference between the learned one fixed channel and the actual channel increases, the performance of the autoencoder learned for one fixed channel is attenuated.
- the difference (error) [ ⁇ 2] between the training channel and the real channel is set based on the target performance, and (i) the quantized channel according to ⁇ 2 (Quantized Channel) and (ii) a method of configuring an auto-encoder set (AE Set) composed of auto-encoders corresponding to each quantized channel.
- any MIMO channel constitutes the complex channel space of can be expressed as here, and denotes the number of antennas at the transmitting end and the number of antennas at the receiving end, respectively.
- setting the target performance to allow the difference ⁇ 2 between the pretrained channel and the real channel means that any real channel included in the area covered by a specific quantized channel on the quantized channel space and the specific quantized channel
- the difference from the channel means that the complex channel space is quantized so that it is always equal to or less than ⁇ 2 within a region covered by the specific quantized channel.
- the cross correlation between H_real and H_trained is the normalized vector (of size 1) of H_real and the reference vector H_trained. It can be calculated by applying the norm operation to the dot product operation of the liver. That is, the cross-correlation can be expressed as in the following equation.
- channel quantization that always satisfies the cross-correlation related to the channel difference ⁇ 2 value that satisfies the target performance defined in the system based on the above relational expression is required.
- the cross-correlation value between any real channel included in the area covered by the specific quantized channel of the quantized channel space and the specific quantized channel is the area covered by the specific quantized channel. must always be quantized to be less than or equal to the value of cross-correlation determined based on the target performance ⁇ 2.
- FIG. 14 is a diagram illustrating an example of a quantized complex channel space. More specifically, FIG. 14 shows a complex channel space is quantized by the RVQ method.
- H(i) may also be called a quantization channel vector, and a codebook generated by RVQ may be defined as a Set of H(i).
- the complex channel space is quantized into a quantization channel space composed of 2q quantization channels H(i).
- 1411 indicates a region covered by one quantization channel vector H(i) included in Set of H(i).
- the quantization channel vector H(i) is a vector with respect to the center of a region covered by the quantization channel vector H(i).
- the quantization bit value q increases as the cross-correlation value determined based on the channel difference ⁇ 2 value increases.
- the complex channel space can be further subdivided and quantized, and at this time, the area covered by one quantization channel vector narrows. Accordingly, the actual channel vectors included in the area covered by the quantization channel vector are located close to the quantization channel vector, so that the cross-correlation value increases.
- the number of pretrained autoencoders that can satisfy the target performance is It can be defined based on the difference ⁇ 2 between the pre-trained channel and the real channel, which is the target performance of the multi-antenna transmission and reception system of . For example, if an error ⁇ 2 between a pretrained channel (quantization channel) and an actual channel is allowed up to 0.5, a cross-correlation of 0.8 should be guaranteed according to the relationship between ⁇ 2 and cross-correlation. Therefore, when the error ⁇ 2 between the pretrained channel (quantization channel) and the actual channel is allowed up to 0.5, the relationship between the number of auto encoders required according to the complex channel dimension can be defined as follows.
- an offline learning-based multi-autoencoder system that can be adaptively applied to the entire channel space can be constructed by pre-training multiple autoencoders that satisfy the above condition.
- a complex channel space can be quantized into a plurality of quantization channels.
- the plurality of quantization channels are quantized to satisfy a specific condition based on an error between an actual channel included in a region covered by any one quantization channel among the plurality of quantization channels and the one quantization channel.
- the specific condition may be a condition in which a cross-correlation value determined based on an error between a real channel and one of the quantization channels is equal to or less than a preset value.
- quantization bits q are derived based on the number of N t N r and a channel error ⁇ 2 that satisfies the target performance.
- Pre-learning is performed on each of the 2 q quantization channels H(i). That is, the autoencoder is trained for each of the 2 q quantization channels H(i).
- channels quantized through a predetermined quantization method may be defined as quantized channel information (QCI), and an index of quantized channels may be referred to as a QCI index. .
- QCI quantized channel information
- a time during which a time varying factor is maintained may be determined based on a Jakes Model. For example, a channel change over time is determined by a Doppler frequency Fd, and a period in which feedback is performed within a time period in which a channel state satisfying a target performance is maintained setting is required.
- the feedback may be feedback including a QCI index based on channel measurement at the receiving end.
- Equation 2 the Time Correlation Coefficient It can be expressed as Equation 2 below by
- ego is the zeroth order Bessel function
- H_real H_trained + ⁇ H where ⁇ H ⁇ CN(0, ⁇ 2)
- the connection of multiple autoencoders and quantized channel feedback is based on the feedback period condition. maintained during That is, the feedback period of the receiver for determining the quantized channel-based multi-auto encoder is the period must be set within In other words, the period from the time when the channel environment is measured During the period, the auto encoder corresponding to the quantization channel corresponding to the predetermined QCI index can exhibit optimal performance, but the period After that, since the channel vector corresponding to the actual channel is outside the area covered by the quantization channel corresponding to the QCI index, the period The selection of the autoencoder should be changed within
- the present method relates to a mapping (connection) method between a plurality of quantized channels configured by quantizing a complex channel space and an auto encoder.
- the transmitter/receiver (Tx/Rx) using an autoencoder with a deep learning-based AI algorithm has a predefined multiple autoencoder (Multiple AE), the transmitter operates the transmitter autoencoder, and the receiver operates the receiver autoencoder. operate the encoder.
- Multiple autoencoders selection of an auto-encoder used for data transmission/reception can be made by selecting a QCI.
- information on auto-encoders learned for each of a plurality of quantization channels configured by quantizing a complex channel space may be previously set in a transmitter/receiver using an auto-encoder.
- Information on auto-encoders may include a mapping relationship between all QCIs corresponding to a quantization channel and all auto-encoders learned for the quantization channel.
- the information on the auto-encoders may be referred to as correspondence relation information, and the correspondence relation information is between a plurality of quantization channels constructed by quantizing a complex channel space and a plurality of pre-learned auto encoders based on the plurality of quantization channels. It may be information related to a correspondence relationship.
- the transmitter/receiver can recognize the determined QCI and the mapped auto-encoder based on the correspondence relationship information.
- FIG. 16 is a diagram illustrating another example of a communication system to which an auto-encoder is applied.
- the communication system of FIG. 16 is different from the communication system of FIG. 13 in that channel quantization is performed for the entire complex channel space and a plurality of auto encoders are used based on the quantized channels.
- FIG. 16 differences from the operation described in FIG. 13 will be mainly described.
- channel estimation and quantization (1621) are performed.
- channel estimation and quantization (1621) are performed.
- a transmitter auto-encoder selection 1612 and a receiver-end auto-encoder selection 1622 are performed at the transmitter/receiver, respectively.
- the receiver may transmit feedback including QCI and CQI to the transmitter.
- the transmitter/receiver may perform data transmission/data reception using the selected auto-encoder.
- This proposal relates to a method of individually quantizing column vectors (channel vectors) of a channel matrix to support spatial multiplexing of MIMO channels.
- Feedback on QCI and CQI performed for each individually quantized column vector (channel vector) may be configured as follows.
- QCC Quantized Channel Codebook
- N quantization channels are configured for each channel vector.
- QCI is configured for each channel vector.
- the QCI for each channel vector can be expressed by the following equation.
- a specific quantization channel c j that maximizes the value of h i c j H for any real channel vector h i may correspond to the real channel vector h i .
- a specific quantization channel c j may correspond to at least one real channel vector h i .
- CQI Channel Quality Information
- QCI and CQI are fed back for each channel vector.
- FIG. 17 is a diagram illustrating an example of a method of quantizing a complex channel space by individually quantizing column vectors of a channel matrix. More specifically, FIG. 17 relates to a case in which the channel matrix is composed of 4 column vectors.
- a channel matrix H 1710 is composed of four channel matrices 1711 to 1714.
- QCIs n1, n2, n3, and n4 are configured, respectively, and CQIs (g(h-- 1 ), g(h-- 2 ), g(h-- 3 ) and g(h-- 4 )) respectively.
- FIG. 18 is a diagram illustrating an example of a method of constructing a corresponding relationship between a quantization channel configured by individually quantizing a column vector of a channel matrix and an auto encoder. More specifically, FIG. 18 relates to a case in which a correspondence relationship is configured such that an auto encoder corresponds to a combination of QCIs corresponding to each channel vector.
- a transmitting auto-encoder and a receiving-end auto-encoder are connected to form one auto-encoder, and a multiple auto-encoder set 1810 composed of a plurality of auto-encoders is formed.
- a QCI combination 1820 including all cases in which QCI indices for each channel vector can be combined is configured.
- the auto encoders included in the multi-auto encoder set 1820 and the QCI combinations included in the QCI combination 1820 are mapped to each other.
- Each of the autoencoders is pretrained for each of the quantization channels specified by a combination of QCIs.
- Information about correspondence between auto-encoders included in the multiple auto-encoder set 1820 and QCI combinations included in the QCI combination 1820 may be previously set in a transmitter/receiver.
- the QCI index fed back from the receiving end to the transmitting end may indicate one quantization channel combination among QCI combinations included in the QCI combination 1820.
- the receiving end and the transmitting end may each determine an auto-encoder to be used for communication based on the corresponding information.
- one QCI exists for one channel vector, and an auto encoder can be selected for each channel vector.
- FIG. 19 is a diagram illustrating another example of a method of constructing a correspondence relationship between a quantization channel configured by individually quantizing a column vector of a channel matrix and an auto encoder. More specifically, FIG. 19 relates to a case in which a correspondence relationship is configured such that one auto-encoder corresponds to each QCI corresponding to each channel vector.
- a transmitting auto-encoder and a receiving-end auto-encoder are connected to form one auto-encoder, and multiple auto-encoder sets (1911 to 1910+Nt) composed of a plurality of auto-encoders form a channel matrix.
- Each of the vectors is constructed.
- QCI indices (1921 to 1920+Nt) corresponding to quantized channels constructed by quantizing the channel vector are configured for each channel vector.
- the auto encoders included in the multi-auto encoder set (1911 to 1910 + Nt) and the QCIs included in the QCI indices (1921 to 1920 + Nt) are mapped to each other.
- each of the auto-encoders is pre-trained for each of the quantization channels specified by the QCI index.
- This proposal relates to a method of vectorizing a channel matrix of a MIMO channel to form one stacked channel vector and quantizing it.
- the stacked channel vector is constructed by vectorizing column vectors of the channel matrix. Accordingly, feedback on QCI and CQI is performed for one stacked channel vector. Feedback on QCI and CQI performed on a stacked channel in which column vectors of a channel matrix are vectorized and quantized may be configured as follows.
- QCC Quantized Channel Codebook
- QCI is constructed for the stacked channel matrix. At this time, the QCI for the stacked channel matrix can be expressed by the following equation.
- a specific quantization channel c j that maximizes the value of h s c j H may correspond to the real channel vector h s there is. Also, a specific quantization channel c j may correspond to at least one real channel matrix h s .
- CQI Channel Quality Information
- QCI and CQI are fed back with respect to one matrix vector.
- FIG. 20 is a diagram illustrating an example of a method of quantizing a complex channel space based on a stacked channel formed by stacking column vectors of a channel matrix. More specifically, FIG. 20 relates to a case of stacking a channel matrix composed of four channel vectors. Referring to FIG. 20, the channel matrix is composed of four channel vectors, and the four channel vectors are stacked to form one stacked channel composed of one vector column (2011 to 2014). Able to know. QCI and CQI can be fed back for one stacked channel.
- channel vectors constituting the channel matrix are stacked and QCI is configured for one channel matrix composed of the stacked channel vectors, one QCI is configured for one channel matrix. Therefore, in this proposal, a method of matching one auto-encoder to a QCI corresponding to one channel matrix is proposed. That is, in this proposal, one auto-encoder can be learned for one stacked quantization channel specified by QCI.
- 21 is a diagram illustrating an example of a method of constructing a correspondence relationship between a stacked quantization channel configured by stacking column vectors of a channel matrix and an auto encoder.
- a transmitting autoencoder and a receiving end autoencoder are connected to form one autoencoder, and a multiple autoencoder set 2111 composed of a plurality of autoencoders 2111-1 to 2111-N is a channel Each of the channel vectors constituting the matrix is configured.
- a CQI index set 2121 is configured with QCI indexes 2121-1 to 2121-N corresponding to stacked quantization channels configured by stacking channel vectors of a channel matrix.
- the auto encoders 2111-1 to 2111-N included in the multi-auto encoder set 2111 and the QCI indexes included in the QCI index set 2121 are mapped to each other.
- Each of the autoencoders is pre-trained for each of the stacked quantization channels specified by a QCI index.
- the QCI may be defined as a method used in an existing communication system or a new method. More specifically, QCI may be defined as one or at least one or more (in combination) of PMI, CQI, RI, and LI, which are components of CSI feedback of an existing communication system.
- the QCI index i is tied to the auto encoder index.
- the auto encoder index and the QCI index may be connected (correspond) in a 1:1 or connected (correspond) in a 1:M manner.
- the number of multiple autoencoders is equal to the number of QCIs, so the complexity of pre-learning can increase, and the transmitter/receiver stores parameters for all autoencoders. It can take up a lot of memory because you have to do it. On the other hand, since auto encoders corresponding to all quantization channels are used, high communication performance can be expected.
- the number of multiple autoencoders is less than the number of QCIs, so the complexity of pre-learning can be reduced, and the transmitter/receiver can store only parameters for some autoencoders. Therefore, memory can be saved.
- one auto-encoder is used for a plurality of quantization channels, communication performance may be lower than when the auto-encoder index and the QCI index are connected (correspond) 1:1.
- Whether or not to communicate based on information may be determined according to a communication environment.
- the multiple auto-encoder system that operates based on the connection relationship between the auto-encoder index and the QCI index described above may operate based on a frequency-division duplexing (FDD) system or a time-division duplexing (TDD) system.
- FDD frequency-division duplexing
- TDD time-division duplexing
- the transmitting end and the receiving end hold predefined multi-auto encoder information.
- the multiple auto-encoder information is obtained between a plurality of quantized channels configured by quantizing a complex channel space as described above and a plurality of auto-encoders pre-trained based on the plurality of quantization channels. It may be information related to a correspondence relationship.
- the detailed structure and parameters of the multi-auto encoder that can be included in the multi-auto encoder information are implemented in the design stage of the transmitting/receiving end device, or broadcast/received in the initial access stage to the communication system. It can be exchanged between transmitting and receiving ends through a multicast/unicast method.
- the detailed structure and parameters of multiple auto encoders can be updated after system launch or in real time through online learning.
- the receiving end performs channel estimation and QCI index calculation based on the reference signal.
- the quantization method for calculating the QCI index may be implemented in the design stage of the transmitter/receiver device, or exchanged between the transmitter/receiver in a broadcast/multicast/unicast method in the initial access stage of the communication system. there is.
- the quantization method for calculating the QCI index may be updated after system launch or updated in real time through online learning.
- the transmitter encodes data to be transmitted through the selected transmitter auto-encoder, and transmits the encoded data to the receiver.
- S2270 The receiving end decodes the signal received from the transmitting end through the selected receiving end auto-encoder.
- the transmitter performs channel estimation and QCI index calculation based on the reference signal.
- the quantization method for calculating the QCI index may be implemented in the design stage of the transmitter/receiver device, or exchanged between the transmitter/receiver in a broadcast/multicast/unicast method in the initial access stage of the communication system. there is.
- the quantization method for calculating the QCI index may be updated after system launch or updated in real time through online learning.
- the transmitter selects (determines) an auto-encoder of the transmitter based on the calculated QCI information.
- the transmitter encodes the data to be transmitted through the selected transmitter auto-encoder, and transmits the encoded data together with the reference signal to the receiver.
- S2360 The receiving end performs channel estimation and QCI index calculation based on the reference signal.
- the receiving end selects the receiving end auto-encoder based on the calculated QCI index.
- the receiving end decodes the signal received from the transmitting end through the selected receiving end auto-encoder.
- the previously described multi-autoencoder has a MIMO Channel Dimension [ Complex Dimension] can be operated in different ways. Since a larger number of quantization bits may be required as the MIMO channel dimension increases, multiple AE sets may be defined in different ways according to the MIMO channel dimension.
- the present specification additionally proposes multi-auto encoder operation that adaptively operates with existing CSI feedback and existing channel coding/modulation.
- the CSI feedback for the existing MIMO channel is composed of a concept quantized by the number of PMI, CQI, RI, and LI bits
- 2 ⁇ number of PMI, CQI, RI, LR bits
- precoders can be used at this time, among the number of cases of 2 ⁇ (number of PMI, CQI, RI, LR bits) for the CSI feedback of the existing communication system, in some cases, the communication system is connected to the pre-learned auto encoder and operates, and the remaining In this case, the communication system may operate by applying the precoder of the existing communication system.
- channel encoding and modulation may be performed by the auto-encoder.
- the communication system operates with the precoder of the existing communication system applied, the channel coding (eg, LDPC, Polar Code, etc.) operation and modulation (eg, BPSK, QAM, etc.) of the existing communication system at the transmitter ) operation is performed, and an operation for signal decoding may be performed at the receiving end.
- the channel coding eg, LDPC, Polar Code, etc.
- modulation eg, BPSK, QAM, etc.
- the complex channel space is quantized by the number of PMI, CQI, RI, and LI bits.
- An auto-encoder is learned for only some quantization channels among all quantization channels of the quantized complex channel space.
- the operation of the existing communication system may be performed for the remaining quantization channels. Accordingly, when the transmitter/receiver transmits/receives CSI feedback composed of PMI, CQI, RI, and LI values corresponding to the partial quantization channels, the transmitter/receiver may operate using an auto encoder. At this time, information on the CSI feedback value connected to the auto encoder may be exchanged between the transmitting/receiving end in advance.
- information on a CSI feedback value connected to an auto-encoder may be information indicating a mapping relationship between a CSI feedback value corresponding to a specific quantized channel and a specific auto-encoder.
- 24 is a flowchart illustrating an example of a method for transmitting data in a wireless communication system using an auto-encoder proposed in this specification.
- a transmitter transmits, to a receiver, (i) channel estimation and (ii) Quantized Channel Information (QCI) associated with determining a specific auto encoder for transmission of the data.
- QCI Quantized Channel Information
- a reference signal for calculating an index is transmitted (S2410).
- the transmitter receives feedback including the QCI index from the receiver (S2420).
- the transmitter transmits the data to the receiver using the specific auto-encoder determined based on (i) the QCI index and (ii) predefined correspondence relationship information (S2430).
- the correspondence relationship information is a correspondence relationship between a plurality of quantized channels configured by quantizing a complex channel space and a plurality of auto encoders pre-trained based on the plurality of quantization channels. , wherein the plurality of quantization channels are quantized to satisfy a specific condition based on an error between an actual channel related to any one quantization channel among the plurality of quantization channels and the one quantization channel.
- 25 illustrates a communication system applied to the present invention.
- a communication system 1 applied to the present invention includes a wireless device, a base station and a network.
- 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.
- a radio access technology eg, 5G New RAT (NR), Long Term Evolution (LTE)
- 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.
- IoT Internet of Thing
- 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.
- the vehicle may include an Unmanned Aerial Vehicle (UAV) (eg, a drone).
- UAV Unmanned Aerial Vehicle
- 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.
- 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 first wireless device 100 and the second wireless device 200 may transmit and receive radio signals through various radio access technologies (eg, LTE, NR).
- ⁇ the first wireless device 100, the second wireless device 200 ⁇ is the ⁇ wireless device 100x, the base station 200 ⁇ of FIG. 25 and/or the ⁇ wireless device 100x, the wireless device 100x.
- ⁇ can correspond.
- FIG. 27 illustrates a signal processing circuit for a transmission signal.
- 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.
- the operations/functions of FIG. 27 may be performed by the processors 102 and 202 and/or the transceivers 106 and 206 of FIG. 27 .
- the hardware elements of FIG. 27 may be implemented in processors 102 and 202 and/or transceivers 106 and 206 of FIG. 26 .
- blocks 1010-1060 may be implemented in the processors 102 and 202 of FIG. 26 .
- blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 26
- block 1060 may be implemented in the transceivers 106 and 206 of FIG. 26 .
- the codeword may be converted into a radio signal through the signal processing circuit 1000 of FIG. 27 .
- 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 .
- the codeword may be converted into a scrambled bit sequence by the scrambler 1010.
- 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.
- N is the number of antenna ports and M is the number of transport layers.
- the precoder 1040 may perform precoding after performing transform precoding (eg, DFT transformation) on complex modulation symbols.
- 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 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. 27 .
- a wireless device may be implemented in various forms according to use-case/service.
- wireless devices 100 and 200 correspond to the wireless devices 100 and 200 of FIG. 26, and include various elements, components, units/units, and/or modules. ) can be configured.
- 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 .
- 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.
- the additional element 140 may be configured in various ways according to the type of wireless device.
- 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.
- the wireless device may be a robot (Fig. 25, 100a), a vehicle (Fig. 25, 100b-1, 100b-2), an XR device (Fig. 25, 100c), a mobile device (Fig. 25, 100d), a home appliance. (FIG. 25, 100e), IoT device (FIG.
- 29 illustrates a portable device applied to the present invention.
- the portable device 100 includes an antenna unit 108, a communication unit 110, a control unit 120, a memory unit 130, a power supply unit 140a, an interface unit 140b, and an input/output unit 140c. ) may be included.
- the antenna unit 108 may be configured as part of the communication unit 110 .
- Blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 28 .
- 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 .
- 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.
- Vehicles or autonomous vehicles may be implemented as mobile robots, vehicles, trains, manned/unmanned aerial vehicles (AVs), ships, and the like.
- AVs manned/unmanned aerial vehicles
- 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. 28 .
- 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 which may include various types of sensors, may obtain vehicle conditions, surrounding environment information, and user information.
- 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.
- a vehicle may be implemented as a means of transportation, a train, an air vehicle, a ship, and the like.
- 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.
- blocks 110 to 130/140a to 140b respectively correspond to blocks 110 to 130/140 of FIG. 28 .
- 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.
- 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.
- HMD head-up display
- a television a television
- smartphone a smartphone
- a computer a wearable device
- a home appliance a digital signage
- a vehicle a robot, and the like.
- 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.
- blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 28 .
- 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.
- 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.
- 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.
- the mobile device 100b may operate as a controller for the XR device 100a.
- 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.
- Robots may be classified into industrial, medical, household, military, and the like depending on the purpose or field of use.
- 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.
- blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 28 .
- 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.
- 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.
- 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.
- 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.
- a communication unit 110 can include Blocks 110 to 130/140a to 140d respectively correspond to blocks 110 to 130/140 of FIG. 28 .
- 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 .
- the memory unit 130 may store data supporting various functions of the AI device 100 .
- the input unit 140a may obtain various types of data from the outside of the AI device 100.
- 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 learning processor unit 140c may perform AI processing together with the learning processor unit of the AI server (FIG. 31, 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 .
- 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.
- An embodiment according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof.
- 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.
- ASICs application specific integrated circuits
- DSPs digital signal processors
- DSPDs digital signal processing devices
- PLDs programmable logic devices
- FPGAs field programmable gate arrays
- processors controllers, microcontrollers, microprocessors, etc.
- 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.
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Abstract
Description
Claims (20)
- 오토 인코더(auto encoder)를 이용하는 무선 통신 시스템에서 송신단이 데이터를 전송하기 위한 방법은,수신단으로, (i) 채널 추정 및 (ii) 상기 데이터의 전송을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 전송하는 단계;상기 수신단으로부터, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 수신하는 단계; 및상기 수신단으로, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 전송하는 단계를 포함하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차에 기초한 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 방법.
- 제 1 항에 있어서,상기 특정 조건은 상기 실제 채널과 상기 어느 하나의 양자화 채널 간의 상기 오차에 기초하여 결정되는 상호 상관(cross correlation)의 값이 사전 설정된 값 이하가 되도록 하는 조건인 것을 특징으로 하는 방법.
- 제 2 항에 있어서,(i) 송신단 안테나 수(Nr)와 수신단 안테나 수(Nt)의 곱(NtNr)및 (ii) 상기 특정 채널과 상기 어느 하나의 양자화 채널 간의 상기 오차에 기초하여 상기 복수의 양자화 채널을 나타내기 위한 양자화 비트 수(q)가 결정되고,상기 복수의 양자화 채널의 개수는 2q 개인 것을 특징으로 하는 방법.
- 제 3 항에 있어서,상기 2q개의 상기 복수의 양자화 채널 각각에 대응되는 오토 인코더들을 사전 학습시키는 단계를 더 포함하되,상기 복수의 오토 인코더는 상기 사전 학습시킨 오토 인코더들로 구성되는 것을 특징으로 하는 방법.
- 제 2 항에 있어서,상기 QCI 인덱스를 포함하는 피드백은 일정 주기로 수신되고,상기 일정 주기는 상기 특정 오토 인코더에 대응되는 특정 양자화 채널이 상기 특정 조건을 만족하는 상태가 유지되는 시간에 기초하여 결정되는 것을 특징으로 하는 방법.
- 제 5 항에 있어서,상기 특정 양자화 채널이 상기 특정 조건을 만족하는 상태가 유지되는 시간은 상기 상호 상관 계수 및 상기 수신단의 이동 속도에 기초하여 결정되는 것을 특징으로 하는 방법.
- 제 6 항에 있어서,상기 수신단으로, 상기 일정 주기를 설정하기 위한 신호를 전송하는 단계를 포함하되,상기 일정 주기는 상기 특정 양자화 채널이 상기 특정 조건을 만족하는 상태가 유지되는 시간보다 작은 값으로 설정되는 것을 특징으로 하는 방법.
- 제 3 항에 있어서,상기 복수의 양자화 채널은 채널 행렬(channel matrix)을 구성하는 적어도 하나의 채널 벡터(channel vector)에 대하여 각각 구성되는 것을 특징으로 하는 방법.
- 제 8 항에 있어서,상기 대응 관계 정보는, 상기 적어도 하나의 채널 벡터에 대하여 각각 구성된 상기 복수의 양자화 채널에 기초하여 조합된 복수의 양자화 채널 조합과 상기 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널 조합과 상기 복수의 오토 인코더가 서로 맵핑되고,상기 양자화 채널 조합은 상기 적어도 하나의 채널 벡터에 각각 대응되는 적어도 하나의 양자화 채널을 포함하는 것을 특징으로 하는 방법.
- 제 9 항에 있어서,상기 QCI 인덱스는 상기 복수의 양자화 채널 조합 중 하나의 양자화 채널 조합을 나타내고,상기 대응 관계 정보에 기초하여, 상기 하나의 양자화 채널 조합에 맵핑되는 하나의 오토 인코더가 상기 특정 오토 인코더로 결정되는 것을 특징으로 하는 방법.
- 제 8 항에 있어서,상기 대응 관계 정보는, 상기 적어도 하나의 채널 벡터에 대하여 각각 구성된 상기 복수의 양자화 채널과 상기 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 적어도 하나의 채널 벡터에 대하여 각각 구성된 상기 복수의 양자화 채널과 상기 복수의 오토 인코더가 서로 맵핑되는 것을 특징으로 하는 방법.
- 제 11 항에 있어서,상기 QCI 인덱스는, 상기 적어도 하나의 채널 벡터 각각에 대하여, 상기 복수의 양자화 채널 중 하나의 양자화 채널을 나타내고,상기 대응 관계 정보에 기초하여, 상기 적어도 하나의 채널 벡터 각각에 대하여, 상기 하나의 양자화 채널에 맵핑되는 하나의 오토 인코더가 결정되고,상기 적어도 하나의 채널 벡터 각각에 대하여 결정된 하나의 오토 인코더가 상기 특정 오토 인코더인 것을 특징으로 하는 방법.
- 제 3 항에 있어서,상기 복수의 양자화 채널은 채널 행렬에 대하여 구성되고,상기 대응 관계 정보는, 상기 채널 행렬에 대하여 구성된 상기 복수의 양자화 채널과 상기 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 채널 행렬에 대하여 구성된 상기 복수의 양자화 채널과 상기 복수의 오토 인코더가 서로 맵핑되는 것을 특징으로 하는 방법.
- 제 13 항에 있어서,상기 QCI 인덱스는 상기 채널 행렬에 대하여 구성된 상기 복수의 양자화 채널 중 하나의 양자화 채널을 나타내고,상기 대응 관계 정보에 기초하여, 상기 하나의 양자화 채널에 맵핑되는 하나의 오토 인코더가 상기 특정 오토 인코더로 결정되는 것을 특징으로 하는 방법.
- 제 1 항에 있어서,상기 대응 관계 정보에 기초하여, (i) 상기 복수의 오토 인코더 각각은 상기 복수의 양자화 채널 중 하나의 양자화 채널에 맵핑되거나, (ii) 상기 복수의 오토 인코더 각각은 상기 복수의 양자화 채널 중 적어도 둘 이상의 양자화 채널에 맵핑되는 것을 특징으로 하는 방법.
- 오토 인코더(auto encoder)를 이용하는 무선 통신 시스템에서 신호를 송수신하기 위한 송신단에 있어서,무선 신호를 전송하기 위한 전송기(transmitter);무선 신호를 수신하기 위한 수신기(receiver);적어도 하나의 프로세서; 및상기 적어도 하나의 프로세서에 동작 가능하게 접속 가능하고, 상기 적어도 하나의 프로세서에 의해 실행될 때, 동작들을 수행하는 지시(instruction)들을 저장하는 적어도 하나의 컴퓨터 메모리를 포함하며,상기 동작들은,수신단으로, (i) 채널 추정 및 (ii) 상기 데이터의 전송을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 전송하는 단계;상기 수신단으로부터, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 수신하는 단계; 및상기 수신단으로, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 전송하는 단계를 포함하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차에 기초한 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 송신단.
- 오토 인코더(auto encoder)를 이용하는 무선 통신 시스템에서 수신단이 데이터를 수신하기 위한 방법은,송신단으로부터, (i) 채널 추정 및 (ii) 상기 데이터의 수신을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 수신하는 단계;상기 송신단으로, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 전송하는 단계; 및상기 송신단으로부터, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 수신하는 단계를 포함하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차가 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 방법.
- 오토 인코더(auto encoder)를 이용하는 무선 통신 시스템에서 신호를 송수신하기 위한 수신단에 있어서,무선 신호를 전송하기 위한 전송기(transmitter);무선 신호를 수신하기 위한 수신기(receiver);적어도 하나의 프로세서; 및상기 적어도 하나의 프로세서에 동작 가능하게 접속 가능하고, 상기 적어도 하나의 프로세서에 의해 실행될 때, 동작들을 수행하는 지시(instruction)들을 저장하는 적어도 하나의 컴퓨터 메모리를 포함하며,상기 동작들은,송신단으로부터, (i) 채널 추정 및 (ii) 상기 데이터의 수신을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 수신하는 단계;상기 송신단으로, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 전송하는 단계; 및상기 송신단으로부터, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 수신하는 단계를 포함하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차가 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 수신단.
- 하나 이상의 명령어들을 저장하는 비일시적 컴퓨터 판독 가능 매체(computer readable medium, CRM)에 있어서,하나 이상의 프로세서들에 의해 실행 가능한 하나 이상의 명령어들은 송신단이,수신단으로, (i) 채널 추정 및 (ii) 상기 데이터의 전송을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 전송하도록 하고,상기 수신단으로부터, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 수신하도록 하고,상기 수신단으로, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 전송하도록 하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차에 기초한 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 비일시적 컴퓨터 판독 가능 매체.
- 하나 이상의 메모리들 및 상기 하나 이상의 메모리들과 기능적으로 연결되어 있는 하나 이상의 프로세서들을 포함하는 장치에 있어서,상기 하나 이상의 프로세서들은 상기 장치가,수신단으로, (i) 채널 추정 및 (ii) 상기 데이터의 전송을 위한 특정 오토 인코더의 결정과 관련된 QCI(Quantized Channel Information) 인덱스(index)를 계산하기 위한 참조 신호를 전송하도록 하고,상기 수신단으로부터, 상기 QCI 인덱스를 포함하는 피드백(feedback)을 수신하도록 하고,상기 수신단으로, (i) 상기 QCI 인덱스 및 (ii) 사전 정의된 대응 관계 정보에 기초하여 결정되는 상기 특정 오토 인코더를 이용하여 상기 데이터를 전송하도록 하되,상기 대응 관계 정보는 복소 채널 공간을 양자화(Quantization)하여 구성한 복수의 양자화 채널(Quantized Channel)과 상기 복수의 양자화 채널에 기초하여 사전 학습(pre-training)된 복수의 오토 인코더 간의 대응 관계와 관련된 정보이고,상기 복수의 양자화 채널은, 상기 복수의 양자화 채널 중 어느 하나의 양자화 채널과 관련된 실제 채널과 상기 어느 하나의 양자화 채널 간의 오차에 기초한 특정 조건을 만족하도록 양자화되는 것을 특징으로 하는 장치.
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KR20200126433A (ko) * | 2017-06-19 | 2020-11-06 | 버지니아 테크 인터렉추얼 프라퍼티스, 인크. | 다중 안테나 송수신기를 이용한 무선 송신을 위한 정보의 인코딩 및 디코딩 |
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2021
- 2021-06-23 WO PCT/KR2021/007901 patent/WO2022270651A1/ko active Application Filing
- 2021-06-23 KR KR1020247001124A patent/KR20240023108A/ko active Search and Examination
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