WO2022004927A1 - Procédé d'émission ou de réception de signal avec un codeur automatique dans un système de communication sans fil et appareil associé - Google Patents

Procédé d'émission ou de réception de signal avec un codeur automatique dans un système de communication sans fil et appareil associé Download PDF

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WO2022004927A1
WO2022004927A1 PCT/KR2020/008753 KR2020008753W WO2022004927A1 WO 2022004927 A1 WO2022004927 A1 WO 2022004927A1 KR 2020008753 W KR2020008753 W KR 2020008753W WO 2022004927 A1 WO2022004927 A1 WO 2022004927A1
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weight
information
signal
learning
neural net
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Korean (ko)
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김성진
김병훈
이상림
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엘지전자 주식회사
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Priority to KR1020237000794A priority Critical patent/KR20230031892A/ko
Priority to PCT/KR2020/008753 priority patent/WO2022004927A1/fr
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present specification is for transmitting and receiving a signal using an auto encoder, and more particularly, to a method for transmitting and receiving a signal in a wireless communication system using an auto encoder, and an apparatus therefor.
  • a wireless communication system is a multiple access system that can support 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), 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 neural net-based automatic artificial intelligence transmission/reception method and an apparatus therefor that adapt itself to a current situation with minimal feedback signaling so as to be suitable for various devices and environments.
  • the present specification provides a method for transmitting and receiving a signal in a wireless communication system using an auto encoder, the method performed by a receiving end, the method comprising: receiving a first weight of an encoder neural network of a transmitting end from a transmitting end ; learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method; transmitting updated weight information for the first weight to the transmitter based on the learning; and receiving a signal to which the update weight information is applied from the transmitter.
  • GD gradient descent
  • the learning based on the gradient descent method is characterized in that learning is performed using a difference between a transmission symbol of a transmission signal and a reception symbol of a reception signal with respect to the transmission signal.
  • the method according to the present specification is characterized in that it further comprises the step of applying the update weight information to the receiving end encoder neural net.
  • the update weight information is a difference value between a previous weight value of the first weight and a new weight value of the first weight.
  • the method further comprises transmitting neural net structure information on at least one of the structure of the transmitter-end encoder neural net and the structure of the receiver-end encoder neural net to the transmitter.
  • the neural net structure information is transmitted to the transmitter together with the update weight information.
  • the neural net structure information is transmitted to the transmitter when at least one of the structure of the encoder neural net of the transmitter and the structure of the encoder neural net of the receiver is changed.
  • the structure of the transmitter encoder neural net or the receiver encoder neural net is characterized in that it is changed when a new node is added to a specific layer of the neural net or a certain node is removed.
  • the present specification provides a receiver for transmitting and receiving a signal in a wireless communication system using an auto encoder, comprising: a transmitter for transmitting a wireless 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: a transmitting end encoder receiving a first weight of a neural network from a transmitter; learning the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method; transmitting updated weight information for the first weight to the transmitter based on the learning; and receiving a signal to which the update weight information is applied from the transmitter.
  • GD gradient descent
  • the present specification has an effect of transmitting and receiving minimal feedback by making it suitable for various devices and environments through a neural net-based automatic artificial intelligence transmission/reception method that adapts itself to a specific situation.
  • 1 illustrates physical channels and general signal transmission used in a 3GPP system.
  • FIG. 2 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.
  • FIG 3 is a view showing an example of a perceptron structure.
  • FIG. 4 is a diagram illustrating an example of a multilayer perceptron structure.
  • 5 is a diagram illustrating an example of a deep neural network.
  • FIG. 6 is a diagram illustrating an example of a convolutional neural network.
  • FIG. 7 is a diagram illustrating 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 the operation structure of a recurrent neural network.
  • FIG. 10 is a diagram illustrating an example of an electromagnetic spectrum.
  • FIG. 11 is a diagram showing an example of THz communication application.
  • FIG. 12 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.
  • FIG. 13 is a diagram illustrating an example of a method of generating an optical device-based THz signal.
  • FIG. 14 is a diagram illustrating an example of an optical element-based THz wireless communication transceiver.
  • 15 is a diagram illustrating a structure of a photoinc source-based transmitter.
  • 16 is a diagram illustrating a structure of an optical modulator.
  • FIG 17 shows an example of a wireless communication system for an auto encoder transmission/reception method.
  • FIG. 18 is a diagram illustrating an example of a wireless communication system for a method of transmitting and receiving an automatic encoder proposed in the present specification.
  • 19 is a diagram illustrating another example of a wireless communication system for a method of transmitting and receiving an auto encoder proposed in the present specification.
  • 20 is a diagram illustrating another example of a wireless communication system for a method of transmitting and receiving an automatic encoder proposed in the present specification.
  • FIG. 21 is a diagram illustrating an example of a recycling weight feedback method as in FIG. 21 proposed in the present specification.
  • FIG. 22 is a diagram illustrating an example of a Net-to-Net addition operation proposed in the present specification.
  • 23 is a flowchart illustrating an example of a method for transmitting and receiving a signal in a wireless communication system using an auto-encoder proposed in the present specification.
  • 25 illustrates a wireless device that can be applied to the present invention.
  • 26 illustrates a signal processing circuit for a transmission signal.
  • FIG. 27 shows another example of a wireless device to which the present invention is applied.
  • 29 illustrates a vehicle or an autonomous driving vehicle to which the present invention is applied.
  • FIG. 30 illustrates a vehicle to which the present invention is applied.
  • 33 illustrates an AI device applied to the present invention.
  • 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 a radio technology such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, Evolved UTRA (E-UTRA), and the like.
  • UTRA is part of the Universal Mobile Telecommunications System (UMTS).
  • 3GPP 3rd Generation Partnership Project
  • Long Term Evolution is a part of Evolved UMTS (E-UMTS) using E-UTRA and LTE-A (Advanced)/LTE-A pro is an evolved version of 3GPP LTE.
  • 3GPP NR New Radio or New Radio Access Technology
  • 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.
  • 3GPP 6G may refer to technology after TS Release 17 and/or Release 18.
  • xxx stands for standard document detail number.
  • LTE/NR/6G may be collectively referred to as a 3GPP system.
  • terms, abbreviations, etc. used in the description of the present invention reference may be made to matters described in standard documents published before the present invention. For example, you can refer to the following documents:
  • RRC Radio Resource Control
  • RRC Radio Resource Control
  • a terminal receives information through a downlink (DL) from a base station, and the terminal transmits information through an uplink (UL) to the base station.
  • 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 they transmit and receive.
  • the terminal When the terminal is powered 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 receives a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station, synchronizes with the base station, and obtains information such as a cell ID. Thereafter, the terminal may receive a physical broadcast channel (PBCH) from the base station to obtain intra-cell broadcast information. On the other hand, the UE may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state.
  • 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 receives a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information carried on the PDCCH to obtain more specific system information. It can be done (S12).
  • PDCCH Physical Downlink Control Channel
  • PDSCH Physical Downlink Control 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 a response message to the preamble through the PDCCH and the corresponding PDSCH ((Random Access (RAR)) Response) message)
  • PRACH Physical Random Access Channel
  • RAR Random Access
  • a contention resolution procedure may be additionally performed (S16).
  • the UE After performing the procedure as described above, the UE performs PDCCH/PDSCH reception (S17) and Physical Uplink Shared Channel (PUSCH)/Physical Uplink Control Channel (Physical Uplink) as a general uplink/downlink signal transmission procedure.
  • Control Channel (PUCCH) transmission (S18) may be performed.
  • the UE 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 according to the purpose of use.
  • the control information transmitted by the terminal to the base station through the uplink or received by the terminal from the base station is a downlink/uplink ACK/NACK signal, a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indicator (RI). ) and the like.
  • the UE may transmit the above-described control information such as CQI/PMI/RI through PUSCH and/or PUCCH.
  • the base station transmits a related signal to the terminal through a downlink channel to be described later, and the terminal receives the related signal from the base station through a downlink channel to be 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 available. applies.
  • QPSK Quadrature Phase Shift Keying
  • QAM 16 Quadrature Amplitude Modulation
  • a codeword is generated by encoding the TB.
  • a 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 together with a demodulation reference signal (DMRS), is generated as an OFDM symbol signal, and is transmitted through a corresponding antenna port.
  • DMRS demodulation reference signal
  • the PDCCH carries downlink control information (DCI) and a QPSK modulation method is applied.
  • DCI downlink control information
  • One PDCCH is composed of 1, 2, 4, 8, 16 CCEs (Control Channel Elements) according to an Aggregation Level (AL).
  • One CCE consists of six REGs (Resource Element Groups).
  • One REG is defined as one OFDM symbol and one (P)RB.
  • the UE obtains DCI transmitted through the PDCCH by performing decoding (aka, blind decoding) on the 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 acquire 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 to be described later, and the base station receives the related signal from the terminal through an uplink channel to be described later.
  • PUSCH Physical Uplink Shared Channel
  • PUSCH carries uplink data (eg, UL-shared channel transport block, UL-SCH TB) and/or uplink control information (UCI), and CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform.
  • CP-OFDM Cyclic Prefix - Orthogonal Frequency Division Multiplexing
  • DFT-s-OFDM Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing
  • the UE transmits the PUSCH by applying transform precoding.
  • the UE when transform precoding is not possible (eg, transform precoding is disabled), the UE transmits a PUSCH based on the CP-OFDM waveform, and when transform precoding is possible (eg, transform precoding is enabled), the UE transmits the CP-OFDM PUSCH may be transmitted based on a waveform or a DFT-s-OFDM waveform.
  • PUSCH transmission is dynamically scheduled by a UL grant in DCI, or based on higher layer (eg, RRC) signaling (and/or Layer 1 (L1) signaling (eg, PDCCH)) semi-statically. Can be scheduled (configured grant).
  • PUSCH transmission may be performed on a codebook-based or non-codebook-based basis.
  • the PUCCH carries uplink control information, HARQ-ACK and/or a scheduling request (SR), and may be divided into a plurality of PUCCHs according to the PUCCH transmission length.
  • SR scheduling request
  • 6G (wireless communication) systems have (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 reduce energy consumption of battery-free IoT devices, (vi) ultra-reliable connections, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system can be in four aspects: 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 the requirements of the 6G system.
  • FIG. 2 is a diagram showing an example of a communication structure that can be provided in a 6G system.
  • 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, Low backhaul and It may 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 wireless connectivity than 5G wireless communication systems.
  • URLLC a key feature of 5G, will become an even more important technology by providing an end-to-end delay of less than 1ms in 6G communication.
  • 6G systems will have much better volumetric spectral efficiencies as opposed to frequently used areal spectral efficiencies.
  • the 6G system can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices will not need to be charged separately in the 6G system.
  • New network characteristics in 6G may be as follows.
  • 6G is expected to be integrated with satellites to provide a global mobile population.
  • the integration of terrestrial, satellite and public networks into one wireless communication system is very important for 6G.
  • the 6G wireless network will deliver power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.
  • WIET wireless information and energy transfer
  • Small cell networks The idea of small cell networks was introduced to improve the received signal quality as a result of improved throughput, energy efficiency and spectral efficiency in cellular systems. As a result, small cell networks are essential characteristics for communication systems beyond 5G and Beyond 5G (5GB). Accordingly, the 6G communication system also adopts the characteristics of the small cell network.
  • Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems.
  • a multi-tier network composed of heterogeneous networks improves overall QoS and reduces costs.
  • a backhaul connection is characterized as a high-capacity backhaul network to support high-capacity traffic.
  • High-speed fiber optics and free-space optics (FSO) systems may be possible solutions to this problem.
  • High-precision localization (or location-based service) through communication is one of the functions of the 6G wireless communication system. Therefore, the radar system will be integrated with the 6G network.
  • Softening and virtualization are two important features that underlie the design process in 5GB networks to ensure flexibility, reconfigurability and programmability. In addition, billions of devices can be shared in a shared physical infrastructure.
  • AI The most important and newly introduced technology for 6G systems 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.
  • Incorporating AI into communications can simplify and enhance real-time data transmission.
  • AI can use numerous analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.
  • AI can also play an important role in M2M, machine-to-human and human-to-machine communication.
  • AI can be a rapid communication in BCI (Brain Computer Interface).
  • BCI Brain Computer Interface
  • AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.
  • 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 a fundamental signal processing and communication mechanism.
  • deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and It may include an allocation (allocation) and the like.
  • 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 physical layer of a downlink (DL). In addition, machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • DL downlink
  • machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • Deep learning-based AI algorithms require large amounts of training data to optimize training parameters.
  • a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a wireless channel.
  • signals of the physical layer of wireless communication are complex signals.
  • further research on a neural network for detecting a complex domain signal is needed.
  • Machine learning refers to a set of actions that trains a machine to create a machine that can perform tasks that humans can or cannot do.
  • Machine learning requires data and a learning model.
  • data learning methods can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network learning is to minimize errors in output. Neural network learning repeatedly inputs training data into the neural network, calculates the output and target errors of the neural network for the training data, and backpropagates the neural network error from the output layer of the neural network to the input layer in the direction to reduce the error. ) to update the weight of each node in the neural network.
  • Supervised learning uses training data in which the correct answer is labeled in the training data, and in unsupervised learning, the correct answer may not be labeled in the training data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which categories are labeled for each of the training data.
  • the labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data.
  • the calculated error is back propagated in the 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 change amount of the connection weight of each node to be updated may be determined according to a learning rate.
  • the computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of learning a neural network, a high learning rate can be used to increase the efficiency by allowing the neural network to quickly obtain a certain level of performance, and in the late learning period, a low learning rate can be used to increase the accuracy.
  • the learning method may vary depending on the characteristics of the data. For example, when the purpose of accurately predicting data transmitted from a transmitter in a communication system is at a receiver, 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) methods. have.
  • 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 to apply input vectors to different multidimensional 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 an input value and an output value.
  • An artificial neural network in which H (d+1)-dimensional perceptrons exist between the 1st layer and the 2nd layer and K (H+1)-dimensional perceptrons exist 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 layers located between the input layer and the output layer are called the hidden layer.
  • the input layer is counted except for the input layer, so it can be viewed as a total of two layers.
  • the artificial neural network is constructed by connecting the perceptrons of the basic blocks in two dimensions.
  • the aforementioned input layer, hidden layer, and output layer can be jointly applied in various artificial neural network structures such as CNN and RNN to be described later as well as multi-layer perceptron.
  • various artificial neural network structures such as CNN and RNN to be described later as well as multi-layer perceptron.
  • the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning.
  • an artificial neural network used for deep learning is called a deep neural network (DNN).
  • DNN deep neural network
  • the deep neural network shown in FIG. 5 is a multilayer perceptron composed of eight hidden layers + output layers.
  • the multi-layered perceptron structure is referred to as a fully-connected neural network.
  • a connection relationship does not exist between nodes located in the same layer, and a connection relationship exists only between nodes located in adjacent layers.
  • DNN has a fully connected neural network structure and is composed of a combination of a number of hidden layers and activation functions, so it can be usefully applied to figure out the correlation between input and output.
  • the correlation characteristic may mean a joint probability of input/output.
  • 5 is a diagram illustrating an example of a deep neural network.
  • various artificial neural network structures different from the above-described DNN can be formed depending on how a plurality of perceptrons are connected to each other.
  • nodes located inside one layer are arranged in a one-dimensional vertical direction.
  • the nodes are two-dimensionally arranged with w horizontally and h vertical nodes (convolutional neural network structure of FIG. 6 ).
  • a weight is added per connection in the connection process from one input node to the hidden layer, a total of h ⁇ w weights must be considered.
  • h ⁇ w nodes in the input layer a total of h2w2 weights are needed between two adjacent layers.
  • FIG. 6 is a diagram illustrating 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 the connection of all modes between adjacent layers, it is assumed that a small filter exists in FIG. As in Fig., the weighted sum and activation function calculations are performed on the overlapping filters.
  • One filter has a weight corresponding to the number corresponding to its size, and weight learning can be performed so that a specific feature on an image can be extracted and output as a factor.
  • a filter with a size of 3 ⁇ 3 is applied to the upper left 3 ⁇ 3 region of the input layer, and an output value obtained by performing weighted sum and activation function operations on the corresponding node is stored in z22.
  • the filter performs weighted sum and activation function operations while scanning the input layer by moving horizontally and vertically at regular intervals, and places the output value at the current filter position.
  • a calculation method is similar to a convolution operation on an image in the field of computer vision, so a deep neural network with such a structure is called a convolutional neural network (CNN), and a hidden layer generated as a result of a convolution operation is called a convolutional layer.
  • a neural network having a plurality of convolutional layers is called a deep convolutional neural network (DCNN).
  • FIG. 7 is a diagram illustrating an example of a filter operation in a convolutional neural network.
  • the number of weights can be reduced by calculating the weighted sum by including only nodes located in the region covered by the filter in the node where the filter is currently located. Due to this, one filter can be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which physical distance in a two-dimensional domain is an important criterion. Meanwhile, in CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through the convolution operation of each filter.
  • a structure in which this method is applied to an artificial neural network is called a recurrent neural network structure.
  • a recurrent neural network connects elements (x1(t), x2(t), ,..., xd(t)) of a certain gaze t on a data sequence to a fully connected neural network.
  • the immediately preceding time point t-1 is a structure in which the weighted sum and activation functions are applied by inputting the hidden vectors (z1(t1), z2(t1), ..., zH(t1)) together.
  • the reason why the hidden vector is transferred to the next time point in this way is that information in the input vector at previous time points is considered to be accumulated in the hidden vector of the current time point.
  • FIG. 8 shows an example of a neural network structure in which a cyclic loop exists.
  • the recurrent neural network operates in a predetermined time sequence with respect to an input data sequence.
  • the hidden vector (z1(1), z2(1),.. .,zH(1)) is input together with the input vector (x1(2),x2(2),...,xd(2)) of the time point 2 and the vector of the hidden layer (z1( 2),z2(2) ,...,zH(2)) are determined. This process is repeatedly performed until time point 2, time point 3, ,,, and time T.
  • FIG. 9 shows an example of the operation structure of a recurrent neural network.
  • a deep recurrent neural network when a plurality of hidden layers are arranged in a recurrent neural network, this is called a deep recurrent neural network (DRNN).
  • the recurrent neural network is 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), Deep Q-Network and It includes various deep learning techniques such as, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • RBM Restricted Boltzmann Machine
  • DNN deep belief networks
  • Deep Q-Network includes various deep learning techniques such as, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • 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 a fundamental signal processing and communication mechanism.
  • deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and It may include an allocation (allocation) and the like.
  • the data rate can be increased by increasing the bandwidth. This can be accomplished by using sub-THz communication with a wide bandwidth and applying advanced large-scale MIMO technology.
  • THz waves also known as sub-millimeter radiation, typically exhibit a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm.
  • the 100GHz-300GHz band range (Sub THz band) is considered a major part of the THz band for cellular communication. If it is added to the sub-THz band and mmWave band, the 6G cellular communication capacity is increased.
  • 300GHz-3THz is in the far-infrared (IR) frequency band.
  • the 300GHz-3THz band is part of the broadband, but at the edge of the wideband, just behind the RF band. Thus, this 300 GHz-3 THz band shows similarities to RF. 10 shows an example of an electromagnetic spectrum.
  • THz communication The main characteristics of THz communication include (i) widely available bandwidth to support very high data rates, and (ii) high path loss occurring at high frequencies (high directional antennas are indispensable).
  • the narrow beamwidth produced by the highly directional antenna reduces interference.
  • the small wavelength of the THz signal allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This allows the use of advanced adaptive nesting techniques that can overcome range limitations.
  • OWC technology is envisioned for 6G communications in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul/fronthaul network connections.
  • OWC technology has already been used since the 4G communication system, but will be used more widely to meet the needs of the 6G communication system.
  • OWC technologies such as light fidelity, visible light communication, optical camera communication, and FSO communication based on a light band are well known technologies.
  • Communication based on optical radio technology can provide very high data rates, low latency and secure communication.
  • LiDAR can also be used for ultra-high-resolution 3D mapping in 6G communication based on wide bands.
  • FSO The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network.
  • data transmission in an FSO system is similar to that of a fiber optic system. Therefore, FSO can be a good technology to provide backhaul connectivity in 6G systems along with fiber optic networks.
  • FSO supports high-capacity backhaul connections for remote and non-remote areas such as sea, space, underwater, and isolated islands.
  • FSO also supports cellular BS connectivity.
  • MIMO technology improves, so does the spectral efficiency. Therefore, large-scale MIMO technology will be important in 6G systems. Since the MIMO technology uses multiple paths, a multiplexing technique and a beam generation and operation technique suitable for the THz band should also be considered important so that a data signal can be transmitted through one or more paths.
  • Blockchain will become an important technology for managing large amounts of data in future communication systems.
  • Blockchain is a form of distributed ledger technology, which is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger.
  • the blockchain is managed as a peer-to-peer network. It can exist without being managed by a centralized authority or server. Data on the blockchain is collected together and organized into blocks. Blocks are linked together and protected using encryption.
  • Blockchain in nature perfectly complements IoT at scale with improved interoperability, security, privacy, reliability and scalability. Therefore, blockchain technology provides several features such as interoperability between devices, traceability of large amounts of data, autonomous interaction of different IoT systems, and large-scale connection stability of 6G communication systems.
  • the 6G system integrates terrestrial and public networks to support vertical expansion of user communications.
  • 3D BS will be provided via low orbit satellites and UAVs. Adding a new dimension in terms of elevation and associated degrees of freedom makes 3D connections significantly different from traditional 2D networks.
  • UAVs Unmanned Aerial Vehicles
  • a BS entity is installed in the UAV to provide cellular connectivity.
  • UAVs have certain features not found in fixed BS infrastructure, such as easy deployment, strong line-of-sight links, and degrees of freedom with controlled mobility.
  • UAVs can *?* easily handle these situations.
  • UAV will become a new paradigm in the field of wireless communication. This technology facilitates the three basic requirements of wireless networks: eMBB, URLLC and mMTC.
  • UAVs can also serve several purposes, such as improving network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, incident monitoring, and more. Therefore, UAV technology is recognized as one of the most important technologies for 6G communication.
  • Tight integration of multiple frequencies and heterogeneous communication technologies is very important in 6G systems. As a result, users can seamlessly move from one network to another without having to make any manual configuration on the device. The best network is automatically selected from the available communication technologies. This will break the limitations of the cell concept in wireless communication. Currently, user movement from one cell to another causes too many handovers in high-density networks, causing handover failures, handover delays, data loss and ping-pong effects. 6G cell-free communication will overcome all of this and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies and different heterogeneous radios of devices.
  • WIET uses the same fields and waves as wireless communication systems.
  • the sensor and smartphone will be charged using wireless power transfer during communication.
  • WIET is a promising technology for extending the life of battery-charging wireless systems. Therefore, devices without batteries will be supported in 6G communication.
  • An autonomous wireless network is a function that can continuously detect dynamically changing environmental conditions and exchange information between different nodes.
  • sensing will be tightly integrated with communications to support autonomous systems.
  • each access network is connected by backhaul connections such as fiber optic and FSO networks.
  • backhaul connections such as fiber optic and FSO networks.
  • Beamforming is a signal processing procedure that adjusts an antenna array to transmit a radio signal in a specific direction.
  • Beamforming technology has several advantages such as high call-to-noise ratio, interference prevention and rejection, and high network efficiency.
  • Hologram beamforming (HBF) is a new beamforming method that is significantly different from MIMO systems because it uses a software-defined antenna. HBF will be a very effective approach for efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.
  • Big data analytics is a complex process for analyzing various large data sets or big data. This process ensures complete data management by finding information such as hidden data, unknown correlations and customer propensity. Big data is gathered from a variety of sources such as videos, social networks, images and sensors. This technology is widely used to process massive amounts of data in 6G systems.
  • the LIS is an artificial surface made of electromagnetic materials, and can change the propagation of incoming and outgoing radio waves.
  • LIS can be seen as an extension of massive MIMO, but the array structure and operation mechanism are different from those of massive MIMO.
  • LIS has low power consumption in that it operates as a reconfigurable reflector with passive elements, that is, only passively reflects the signal without using an active RF chain.
  • each of the passive reflectors of the LIS must independently adjust the phase shift of the incoming signal, it can be advantageous for a wireless communication channel.
  • the reflected signal can be gathered at the target receiver to boost the received signal power.
  • THz Terahertz
  • FIG. 10 is a diagram illustrating an example of an electromagnetic spectrum.
  • THz wave is located between RF (Radio Frequency)/millimeter (mm) and infrared band, (i) It transmits non-metal/non-polar material better than visible light/infrared light, and has a shorter wavelength than RF/millimeter wave, so it has high straightness. Beam focusing may be possible.
  • the photon energy of the THz wave is only a few meV, it is harmless to the human body.
  • the frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band with low propagation loss due to absorption of molecules in the air.
  • the standardization discussion on THz wireless communication is being discussed centered on the IEEE 802.15 THz working group in addition to 3GPP, and the standard documents issued by the IEEE 802.15 Task Group (TG3d, TG3e) may specify or supplement the content described in this specification. have.
  • THz wireless communication may be applied to wireless recognition, sensing, imaging, wireless communication, THz navigation, and the like.
  • 11 is a diagram showing an example of THz communication application.
  • a THz wireless communication scenario may be classified into a macro network, a micro network, and a nanoscale network.
  • THz wireless communication can be applied to vehicle-to-vehicle connection and backhaul/fronthaul connection.
  • THz wireless communication in micro networks is applied to indoor small cells, fixed point-to-point or multi-point connections such as wireless connections in data centers, and near-field communication such as kiosk downloading.
  • Table 2 below is a table showing an example of a technique that can be used in the THz wave.
  • THz wireless communication can be classified based on a method for generating and receiving THz.
  • the THz generation method can be classified into an optical device or an electronic device-based technology.
  • 12 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.
  • a method of generating THz using an electronic device includes a method using a semiconductor device such as a Resonant Tunneling Diode (RTD), a method using a local oscillator and a multiplier, and an integrated circuit based on a compound semiconductor HEMT (High Electron Mobility Transistor).
  • MMIC Monolithic Microwave Integrated Circuits
  • a doubler, tripler, or multiplier is applied to increase the frequency, and it is radiated by the antenna through the subharmonic mixer. Since the THz band forms a high frequency, a multiplier is essential.
  • the multiplier is a circuit that has an output frequency that is N times that of the input, matches the desired harmonic frequency, and filters out all other frequencies.
  • beamforming may be implemented by applying an array antenna or the like to the antenna of FIG. 12 .
  • IF denotes an intermediate frequency
  • tripler denote a multiplier
  • PA Power Amplifier denotes
  • LNA denotes a low noise amplifier
  • PLL denotes a phase lock circuit (Phase). -Locked Loop).
  • FIG. 13 is a diagram illustrating an example of a method of generating an optical device-based THz signal
  • FIG. 14 is a diagram illustrating an example of an optical device-based THz wireless communication transceiver.
  • Optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device.
  • the optical element-based THz signal generation technology is a technology that generates a high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector.
  • it is easier to increase the frequency compared to the technology using only electronic devices, it is possible to generate a high-power signal, and it is possible to obtain a flat response characteristic in a wide frequency band.
  • a laser diode, a broadband optical modulator, and a high-speed photodetector are required to generate an optical device-based THz signal. In the case of FIG.
  • an optical coupler refers to a semiconductor device that uses light waves to transmit electrical signals to provide coupling with electrical insulation between circuits or systems
  • UTC-PD Uni-Traveling Carrier Photo-) Detector
  • UTC-PD is one of the photodetectors, which uses electrons as active carriers and reduces the movement time of electrons by bandgap grading.
  • UTC-PD is capable of photodetection above 150GHz.
  • EDFA Erbium-Doped Fiber Amplifier
  • PD Photo Detector
  • OSA various optical communication functions (photoelectric It represents an optical module (Optical Sub Aassembly) in which conversion, electro-optical conversion, etc.) are modularized into one component
  • DSO represents a digital storage oscilloscope.
  • FIGS. 15 and 16 illustrate the structure of the photoelectric converter (or photoelectric converter) will be described with reference to FIGS. 15 and 16 .
  • 15 illustrates a structure of a photoinc source-based transmitter
  • FIG. 16 illustrates a structure of an optical modulator.
  • a phase of a signal may be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing electrical characteristics through a microwave contact or the like. Accordingly, an optical modulator output is formed as a modulated waveform.
  • the photoelectric modulator (O/E converter) is an optical rectification operation by a nonlinear crystal (nonlinear crystal), photoelectric conversion (O / E conversion) by a photoconductive antenna (photoconductive antenna), a bunch of electrons in the light beam (bunch of) THz pulses can be generated by, for example, emission from relativistic electrons.
  • a terahertz pulse (THz pulse) generated in the above manner may have a length in units of femtoseconds to picoseconds.
  • An O/E converter performs down conversion by using non-linearity of a device.
  • a number of contiguous GHz bands for fixed or mobile service use for the terahertz system are used. likely to use
  • available bandwidth may be classified based on oxygen attenuation of 10 ⁇ 2 dB/km in a spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of several band chunks may be considered.
  • the bandwidth (BW) becomes about 20 GHz.
  • Effective down conversion from the IR band to the THz band depends on how the nonlinearity of the O/E converter is utilized. That is, in order to down-convert to a desired terahertz band (THz band), the O/E converter having the most ideal non-linearity for transfer to the terahertz band (THz band) is design is required. If an O/E converter that does not fit the target frequency band is used, there is a high possibility that an error may occur with respect to the amplitude and phase of the corresponding pulse.
  • a terahertz transmission/reception system may be implemented using one photoelectric converter. Although it depends on the channel environment, as many photoelectric converters as the number of carriers may be required in a far-carrier system. In particular, in the case of a multi-carrier system using several broadbands according to the above-described spectrum usage-related scheme, the phenomenon will become conspicuous. In this regard, a frame structure for the multi-carrier system may be considered.
  • the down-frequency-converted signal based on the photoelectric converter may be transmitted in a specific resource region (eg, a specific frame).
  • the frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).
  • 6G mobile communication is expected to be used in various communication environments and transmitting/receiving devices.
  • the transmitting/receiving device needs to optimize a communication method suitable for the corresponding environment by itself.
  • the current transmission/reception method (or device), which is a fixed rule-based type, requires expert intervention or change of transmission/reception protocol in order to adapt to a new environment.
  • a neural net-based transmission/reception method which is a learning method
  • the structure can be changed when learning the neural net, so that it can guarantee a more optimal state in terms of performance and complexity. Accordingly, the present specification provides a neural net-based automatic artificial intelligence transceiver and method that adapts itself to a real situation with minimal feedback signaling to be suitable for various devices and environments.
  • FIG 17 shows an example of a wireless communication system for an auto encoder transmission/reception method.
  • a transmitting end encoder neural network and a receiving end decoder neural net are used instead of the transmitting and receiving methods designed by experts for the transmitting end and the receiving end, respectively. While it takes a lot of effort and development period of experts to design an optimal communication method for a given environment, the existing auto-encoder transmission/reception method is to design a transmission/reception method without an expert's effort.
  • the receiving end learns the two neural net weights of the auto encoder by the GD (Gradient Descent) method based on the difference between the transmit symbol and the received symbol, and then sends these two encoder neural nets and the decoder neural net to the transmitting end in the actual communication process. and the receiver, respectively.
  • GD Gradient Descent
  • the GD method is a learning method used to optimize weight parameters in a neural network
  • stochastic gradient descent (SGD) is a learning method that finds an optimal value by rotating it little by little as much as a mini-batch size.
  • Equation 1 An example of an update method of the GD method, that is, stochastic gradient descent (SGD) may be expressed by Equation 1 below.
  • the GD method uses a method of collecting, averaging, and differentiating the error magnetism as much as the batch size (N) and then updating the weight.
  • s and r indicate messages transmitted and received during a certain period (frame size).
  • j_loss(s,r) is the loss suitable for the purpose, such as MMSE (minimum mean-square-error), cross entropy, BLER (block error rate), FER (frame error rate), LLR (log-likelihood ratio), etc. It can be set as a loss function.
  • the loss function may mean a function indicating an error in the current weight, that is, an error.
  • the existing automatic encoder transmission/reception method of FIG. 17 may have the following limitations.
  • the weight of the transmitter encoder neural net among the two neural networks learned by the receiver should be efficiently fed back to the transmitter (or the transmitter or the transmitter).
  • the existing auto encoder has a network structure designed by experts, but the network structure must be automatically optimized to adapt to a given communication environment.
  • FIG. 18 is a diagram illustrating an example of a wireless communication system for a method of transmitting and receiving an automatic encoder proposed in the present specification.
  • the adaptive auto-encoder transmission/reception method of FIG. 18 is a method for adapting the auto-encoder transmission/reception method ( FIG. 17 ) proposed in the existing method to a wireless communication channel situation through weighted feedback.
  • the receiving end learns the two neural net weights of the auto encoder using the GD method as the difference between the transmit symbol and the received symbol.
  • An example of an update method of the GD method that is, stochastic gradient descent (SGD), is the same as Equation 1 salpinned above, so it will be referred to.
  • SGD stochastic gradient descent
  • the GD method uses a method of collecting, averaging, and differentiating the error magnetism as much as the batch size (N) and then updating the weight.
  • s and r indicate messages transmitted and received during a certain period (frame size), and j_loss(s, r) can be set as a loss function suitable for a purpose such as MMSE, cross entropy, BLER, FER, and LLR.
  • the receiving end feeds back the weight of the transmitter encoder neural net to the transmitting end.
  • the transmitting end and the receiving end neural nets use newly updated weights, respectively.
  • the salpin adaptive automatic encoder transmission/reception method in FIG. 18 may have the following limitations.
  • the receiving end transmits the updated weight information to the transmitting end neural net
  • the obtained weight value is directly fed back instead of the difference from the previously transmitted information, which may cause a lot of loss of the uplink control channel.
  • the method proposed in FIG. 18 can adaptively update the neural networks of the transmitter and receiver even when the communication situation changes, so that performance can be guaranteed to some extent even in the changed situation.
  • the structure of the neural net used is not adaptively changed, there is a limit to adapting when a large change occurs. In other words, reducing or increasing the number of nodes to change the complexity is not considered, so there is a limit to optimizing performance.
  • 19 is a diagram illustrating another example of a wireless communication system for a method of transmitting and receiving an auto encoder proposed in the present specification.
  • FIG. 19 relates to a wireless communication system for another automatic encoder transmission/reception method, that is, a wireless communication system for a limited feedback adaptive automatic auto encoder transmission/reception method.
  • the receiving end learns the two neural net weights of the auto-encoder through the GD method using the difference between the transmit symbol and the received symbol.
  • An example of the update method of the GD method that is, the stochastic gradient descent (SGD) method is the same as Equation 1 described with reference to FIG. 17, and thus Equation 1 will be referred to.
  • SGD stochastic gradient descent
  • Equation 2 the difference from the previous update may be defined as in Equation 2 below.
  • the neural net weight update part is the difference between the new weight and the previous weight of the transmitter encoder neural net. is fed back to the transmitter.
  • the transmitting end Newrelnet adds the new weight difference information to the previous weight information and updates it with the new weight.
  • the receiving end neural net uses each updated weight directly.
  • the neural net weight update part changes the structure of the neural net in order to improve transmission/reception performance or reduce the complexity of neural net processing, and reflects the changed structure information to the neural net at the transmitting end and the neural net at the receiving end.
  • the transmitting end neural net feeds back the weights together with the weights to the receiving end, in particular, the neural net weight update part through the uplink control channel.
  • the automatic encoder transmission/reception method proposed in FIG. 19 may have the following limitations.
  • 20 is a diagram illustrating another example of a wireless communication system for a method of transmitting and receiving an automatic encoder proposed in the present specification.
  • FIG. 20 relates to an effective limited feedback adaptive auto encoder transmission/reception method.
  • the method proposed in FIG. 20 is to keep the uplink feedback information constant even if there is a change in the structure of the neural net in the auto encoder transmission/reception method of FIG. 19 and to effectively update the structure in order to reduce the amount of feedback information.
  • the receiving end learns the two neural net weights of the auto encoder by the GD method using the difference between the transmitted symbol and the received symbol.
  • the neural net weight update part is the difference between the new weight and the previous weight of the transmitter encoder neural net. is fed back to the transmitter.
  • the transmitting end neural net updates the new weight information by adding the newly received weight difference information to the previous weight information, and the receiving end neural net uses each updated weight directly.
  • the neural net weight update part of FIG. 20 changes the structure of the neural net in order to further increase transmission/reception performance or reduce the complexity of neural net processing, and reflect the changed structure information to the neural net of the transmitter and the neural net of the receiver. Then, the neural net weight update part is fed back together with the weight to the transmitting end neural net through an uplink control channel.
  • the automatic encoder transmission/reception method proposed in FIG. 20 has the following differences from other automatic encoder transmission/reception methods discussed above.
  • the output result of the new network increased or decreased using the net2net operation is determined by the network of the previous structure. Make it the same or similar to the processing result so that previous weights can be reused. As a result, the newly obtained weight does not change much compared to the previous weight.
  • it is a method that introduces the concept of transfer learning.
  • the weight is not reset despite the structure change, so feedback is possible by applying the recycle weight feedback scheme as shown in FIG. Accordingly, the amount of weights to be fed back does not increase even at the time of structure change.
  • the transmitting end itself performs the net2net operation according to the situation, so that additional signaling for performing the net2net operation is not required.
  • FIG. 21 is a diagram illustrating an example of a recycling weight feedback method as in FIG. 21 proposed in the present specification.
  • the Net2net add operation makes the previous weights reused by making the processing result by the previously small number of nodes and the output result of the network to which the nodes have been added the same, so that the newly obtained weight is highly correlated with the previous weight.
  • the net2net subtract operation makes the output value identical to the output value of the previous node even when the number of nodes is reduced when a node of a specific layer of the transmitting-end neural net is removed for performance optimization or complexity adjustment.
  • the node since the node has been removed, it may not be possible to completely match it, but the change in the old weight values and the new weight values is made as small as possible so that the feedback change amount due to the structural change due to the node removal is not large.
  • a basic method linear
  • an advanced method using NN
  • a simple method etc.
  • FIG. 22 is a diagram illustrating an example of a Net-to-Net addition operation proposed in the present specification.
  • the added node (h[3],2210) determines initial values of the input weight and the output weight by using an existing node (h[2], 2220) adjacent thereto.
  • the input weights of the additional node (h[3]) are the same as those of the existing node (h[2]) (c, d), and the output weights are obtained by dividing the weight of the existing node (h[2]) in half (f /2) It is used by replacing the weights of the existing node and the new node. That is, if it is expressed as an equation, it is as shown in Equations 3 and 4 below.
  • net2net subtract operation There are a basic method, a simple method, and an advanced method for net2net subtract operation, and we will look at them in turn.
  • the simple method uses the previous weights as they are after node removal. From a performance point of view, it should be optimally used after reset, but using the concept of transfer learning, learning is carried out while maintaining the previous weights to minimize the time consumed in learning and to make the weight change small.
  • Advanced methods use neural net methods to increase accuracy.
  • an advanced method using a neural net is to take the outputs (x[1], x[2]) of the previous (input) layer as input, and the output is the output value (y) of the next (output) layer. Create a small neural net to find c, d, and f. For this method, after the previous learning is completed, the output of the two stages is calculated and stored. In particular, when a node structure change request comes, the request is processed by the transmitter. Since the output is limited to the neural net output of the transmitter, there is an advantage that it can be calculated by the transmitter itself.
  • the node having the largest index among the nodes sharing the input with the node N is compensated.
  • the output weight is added as much as the output weight of the node N, and when the output weights of the 2nd and 3rd nodes are expressed as f, f', after the 3rd node is removed f becomes f + f'.
  • the input weight of the node to be compensated for is either (1) just left unchanged (ignoring the effect that the removed node refines and the node to be compensated considers its optimal value to be the same as the optimal value of the removed node) or (2) ) Change to the weighted average of the input weights of the removed node and the compensated node.
  • Another method is to give the weights by 1/2, so that c is c/2 + c'/2 and d is d/2 + d'/2.
  • Compensation method 1 when there are K nodes to be compensated, attenuates the output weight of the removed node by 1/K and adds it to the output weight of the K nodes, and the input weights of the nodes to be compensated are (1) above or (2) method.
  • 23 is a flowchart illustrating an example of a method for transmitting and receiving a signal in a wireless communication system using an auto-encoder proposed in the present specification.
  • the receiving end receives the first weight of the transmitting end encoder neural network from the transmitting end (S2310).
  • the receiving end may be expressed as a receiving device, a terminal, a receiving unit, or the like.
  • the receiving end learns the first weight and the second weight of the receiving end encoder neural net based on a gradient descent (GD) method (S2320).
  • GD gradient descent
  • the learning based on the gradient descent method may include learning using a difference between a transmission symbol of a transmission signal and a reception symbol of a reception signal with respect to the transmission signal.
  • the receiving end transmits the updated updated weight information for the first weight to the transmitting end based on the learning (S2330).
  • the update weight information may be a differential value between a previous weight value of the first weight and a new weight value of the first weight.
  • the receiving end receives the signal to which the update weight information is applied from the transmitting end (S2340).
  • the receiving end may apply the update weight information to the receiving end encoder neural net.
  • the receiving end may transmit neural net structure information on at least one of the structure of the transmitting end encoder neural net and the receiving end encoder neural network structure to the transmitting end.
  • the receiving end may transmit the neural net structure information together with the update weight information to the transmitting end.
  • the neural net structure information may be transmitted to the transmitting end when at least one of the structure of the transmitting end encoder neural net and the receiving end encoder neural net structure is changed.
  • the structure of the transmitter encoder neural net or the receiver encoder neural net may be changed when a new node is added to a specific layer of the neural net or an arbitrary node is removed.
  • the communication system 1 applied to the present invention includes a wireless device, a base station, and a network.
  • the wireless device refers to a device that performs communication using a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)), and may be referred to as a communication/wireless/5G device.
  • a radio access technology eg, 5G NR (New RAT), LTE (Long Term Evolution)
  • the wireless device may include a robot 100a, a vehicle 100b-1, 100b-2, an eXtended Reality (XR) device 100c, a hand-held device 100d, and a home appliance 100e. ), an Internet of Thing (IoT) device 100f, and an AI device/server 400 .
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving 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 AR (Augmented Reality)/VR (Virtual Reality)/MR (Mixed Reality) devices, and include a Head-Mounted Device (HMD), a Head-Up Display (HUD) provided in a vehicle, a television, a smartphone, It may be implemented in the form of a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like.
  • the portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), a computer (eg, a laptop computer), and the like.
  • Home appliances may include a TV, a refrigerator, a washing machine, and the like.
  • the IoT device may include a sensor, a smart meter, and the like.
  • the base station and the network may be implemented as a wireless device, and the specific wireless device 200a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 300 through the base station 200 .
  • AI Artificial Intelligence
  • the network 300 may be configured using a 3G network, a 4G (eg, LTE) network, or a 5G (eg, NR) network.
  • the wireless devices 100a to 100f may communicate with each other through the base station 200/network 300, but may also communicate directly (e.g. sidelink communication) without passing through the base station/network.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (e.g. Vehicle to Vehicle (V2V)/Vehicle to everything (V2X) communication).
  • the IoT device eg, sensor
  • the IoT device may communicate directly with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.
  • Wireless communication/connection 150a and 150b may be performed between the wireless devices 100a to 100f/base station 200 - the base station 200/wireless devices 100a to 100f.
  • the wireless communication/connection may be performed through various wireless access technologies (eg, 5G NR) for uplink/downlink communication 150a and sidelink communication 150b (or D2D communication).
  • 5G NR wireless access technologies
  • the wireless device and the base station/wireless device may transmit/receive wireless signals to each other.
  • the wireless communication/connection 150a and 150b may transmit/receive signals through various physical channels based on all/part of the process of FIG. 1 .
  • various configuration information setting processes for wireless signal transmission/reception various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.) , at least a part of a resource allocation process may be performed.
  • various signal processing processes eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.
  • 25 illustrates a wireless device that can be applied to the present invention.
  • the first wireless device 100 and the second wireless device 200 may transmit and receive wireless signals through various wireless access technologies (eg, LTE, NR).
  • ⁇ first wireless device 100, second wireless device 200 ⁇ is ⁇ wireless device 100x, base station 200 ⁇ of FIG. 24 and/or ⁇ wireless device 100x, wireless device 100x) ⁇ can be matched.
  • the first wireless device 100 includes one or more processors 102 and one or more memories 104 , and may further include one or more transceivers 106 and/or one or more antennas 108 .
  • the processor 102 controls the memory 104 and/or the transceiver 106 and may be configured to implement the functions, procedures and/or methods described/suggested above. For example, the processor 102 may process information in the memory 104 to generate first information/signal, and then transmit a wireless signal including the first information/signal through the transceiver 106 . In addition, the processor 102 may receive the radio signal including the second information/signal through the transceiver 106 , and then store information obtained from signal processing of the second information/signal in the memory 104 .
  • the memory 104 may be connected to the processor 102 and may store various information related to the operation of the processor 102 .
  • the memory 104 may store software code including instructions for performing some or all of the processes controlled by the processor 102 , or for performing the procedures and/or methods described/suggested above.
  • the processor 102 and the memory 104 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 106 may be coupled to the processor 102 , and may transmit and/or receive wireless signals via one or more antennas 108 .
  • the transceiver 106 may include a transmitter and/or a receiver.
  • the transceiver 106 may be used interchangeably with a radio frequency (RF) unit.
  • a wireless device may refer to a communication modem/circuit/chip.
  • the second wireless device 200 includes one or more processors 202 , one or more memories 204 , and may further include one or more transceivers 206 and/or one or more antennas 208 .
  • the processor 202 controls the memory 204 and/or the transceiver 206 and may be configured to implement the functions, procedures and/or methods described/suggested above. For example, the processor 202 may process the information in the memory 204 to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 206 . In addition, the processor 202 may receive the radio signal including the fourth information/signal through the transceiver 206 , and then store information obtained from signal processing of the fourth information/signal in the memory 204 .
  • the memory 204 may be connected to the processor 202 and may store various information related to the operation of the processor 202 .
  • the memory 204 may store software code including instructions for performing some or all of the processes controlled by the processor 202, or for performing the procedures and/or methods described/suggested above.
  • the processor 202 and the memory 204 may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206 may be coupled to the processor 202 and may transmit and/or receive wireless signals via one or more antennas 208 .
  • the transceiver 206 may include a transmitter and/or a receiver.
  • the transceiver 206 may be used interchangeably with an RF unit.
  • a wireless device may refer to a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors 102 , 202 .
  • one or more processors 102 , 202 may implement one or more layers (eg, functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP).
  • the one or more processors 102 and 202 may generate one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) according to the functions, procedures, proposals and/or methods disclosed herein.
  • PDUs Protocol Data Units
  • SDUs Service Data Units
  • One or more processors 102 , 202 may generate messages, control information, data, or information according to the functions, procedures, proposals and/or methods disclosed herein.
  • the one or more processors 102 and 202 generate a signal (eg, a baseband signal) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed herein. , to one or more transceivers 106 and 206 .
  • One or more processors 102 , 202 may receive signals (eg, baseband signals) from one or more transceivers 106 , 206 , PDUs, SDUs, and/or SDUs according to the functions, procedures, proposals and/or methods disclosed herein. , a message, control information, data or information can be obtained.
  • One or more processors 102, 202 may be referred to as a controller, microcontroller, microprocessor, or microcomputer.
  • One or more processors 102 , 202 may be implemented by hardware, firmware, software, or a combination thereof.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGAs Field Programmable Gate Arrays
  • the functions, procedures, proposals and/or methods disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like.
  • Firmware or software configured to perform the functions, procedures, proposals, and/or methods disclosed herein is contained in one or more processors 102, 202, or stored in one or more memories 104, 204, to one or more processors 102, 202) can be driven.
  • the functions, procedures, proposals and/or methods disclosed in this document may be implemented using firmware or software in the form of code, instructions, and/or a set of instructions.
  • One or more memories 104 , 204 may be coupled with one or more processors 102 , 202 , and may store various forms of data, signals, messages, information, programs, code, instructions, and/or instructions.
  • the one or more memories 104 and 204 may be comprised of ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer readable storage media, and/or combinations thereof.
  • One or more memories 104 , 204 may be located inside and/or external to one or more processors 102 , 202 . Additionally, one or more memories 104 , 204 may be coupled to one or more processors 102 , 202 through various technologies, such as wired or wireless connections.
  • One or more transceivers 106 , 206 may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flowcharts of this document to one or more other devices.
  • the one or more transceivers 106, 206 may receive user data, control information, radio signals/channels, etc., referred to in the functions, procedures, proposals, methods, and/or flowcharts of operations disclosed herein, or the like, from one or more other devices.
  • one or more transceivers 106 , 206 may be coupled to one or more processors 102 , 202 and may transmit and receive wireless signals.
  • one or more processors 102 , 202 may control one or more transceivers 106 , 206 to transmit user data, control information, or wireless signals to one or more other devices.
  • one or more processors 102 , 202 may control one or more transceivers 106 , 206 to receive user data, control information, or wireless signals from one or more other devices.
  • one or more transceivers 106, 206 may be coupled to one or more antennas 108, 208, and the one or more transceivers 106, 206 may be coupled to one or more of the transceivers 106, 206 via the one or more antennas 108, 208 for the functions, procedures, and procedures disclosed herein.
  • one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports).
  • the one or more transceivers 106, 206 convert the received radio signal/channel, etc. from the RF band signal to process the received user data, control information, radio signal/channel, etc. using the one or more processors 102, 202. It can be converted into a baseband signal.
  • One or more transceivers 106 , 206 may convert user data, control information, radio signals/channels, etc. processed using one or more processors 102 , 202 from baseband signals to RF band signals.
  • one or more transceivers 106 , 206 may include (analog) oscillators and/or filters.
  • 26 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. 26 may be performed by the processors 102 , 202 and/or transceivers 106 , 206 of FIG. 26 .
  • the hardware elements of FIG. 26 may be implemented in processors 102 , 202 and/or transceivers 106 , 206 of FIG. 25 .
  • blocks 1010 to 1060 may be implemented in the processors 102 and 202 of FIG. 25 .
  • blocks 1010 to 1050 may be implemented in the processors 102 and 202 of FIG. 25
  • block 1060 may be implemented in the transceivers 106 and 206 of FIG. 25 .
  • the codeword may be converted into a wireless signal through the signal processing circuit 1000 of FIG. 26 .
  • the codeword is a coded bit sequence of an information block.
  • the information block may include a transport block (eg, a UL-SCH transport block, a DL-SCH transport block).
  • 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 .
  • a scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device, and the like.
  • the scrambled bit sequence may be modulated by a modulator 1020 into a modulation symbol sequence.
  • the modulation method may include pi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying (m-PSK), m-Quadrature Amplitude Modulation (m-QAM), and the like.
  • the complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 1030 .
  • Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 1040 (precoding).
  • the output z of the precoder 1040 may be obtained by multiplying the output y of the layer mapper 1030 by the precoding matrix W of N*M.
  • N is the number of antenna ports
  • M is the number of transport layers.
  • the precoder 1040 may perform precoding after performing transform precoding (eg, DFT transform) on the complex modulation symbols. Also, the precoder 1040 may perform precoding without performing transform precoding.
  • the resource mapper 1050 may map modulation symbols of each antenna port to a time-frequency resource.
  • the time-frequency resource may include a plurality of symbols (eg, a CP-OFDMA symbol, a DFT-s-OFDMA symbol) in the time domain and a plurality of subcarriers in the frequency domain.
  • CP Cyclic Prefix
  • DAC Digital-to-Analog Converter
  • the signal processing process for the received signal in the wireless device may be configured in reverse of the signal processing processes 1010 to 1060 of FIG. 26 .
  • the wireless device eg, 100 and 200 in FIG. 25
  • the received radio signal may be converted into a baseband signal through a signal restorer.
  • the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module.
  • ADC analog-to-digital converter
  • FFT Fast Fourier Transform
  • the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a descrambling process.
  • the codeword may be restored to the original information block through decoding.
  • the signal processing circuit (not shown) for the received signal may include a signal restorer, a resource de-mapper, a post coder, a demodulator, a descrambler, and a decoder.
  • FIG. 27 shows another example of a wireless device to which the present invention is applied.
  • the wireless device may be implemented in various forms according to use-examples/services.
  • wireless devices 100 and 200 correspond to wireless devices 100 and 200 of FIG. 25 , and include various elements, components, units/units, and/or modules. ) can be composed of
  • 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 .
  • communication circuitry 112 may include one or more processors 102 , 202 and/or one or more memories 104 , 204 of FIG. 25 .
  • transceiver(s) 114 may include one or more transceivers 106 , 206 and/or one or more antennas 108 , 208 of FIG. 25 .
  • the control unit 120 is electrically connected to the communication unit 110 , the memory unit 130 , and the additional element 140 , and controls general operations of the wireless device.
  • the controller 120 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 130 .
  • control unit 120 transmits information stored in the memory unit 130 to the outside (eg, other communication device) through the communication unit 110 through a wireless/wired interface, or externally (eg, through the communication unit 110 ) Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 130 .
  • the additional element 140 may be configured in various ways according to the type of the wireless device.
  • the additional element 140 may include at least one of a power unit/battery, an input/output unit (I/O unit), a driving unit, and a computing unit.
  • the wireless device may include a robot ( FIGS. 24 and 100a ), a vehicle ( FIGS. 24 , 100b-1 , 100b-2 ), an XR device ( FIGS. 24 and 100c ), a mobile device ( FIGS. 24 and 100d ), and a home appliance. (FIG. 24, 100e), IoT device (FIG.
  • digital broadcasting terminal digital broadcasting terminal
  • hologram device public safety device
  • MTC device medical device
  • fintech device or financial device
  • security device climate/environment device
  • It may be implemented in the form of an AI server/device ( FIGS. 24 and 400 ), a base station ( FIGS. 24 and 200 ), and a network node.
  • the wireless device may be mobile or used in a fixed location depending on the use-example/service.
  • various elements, components, units/units, and/or modules in the wireless devices 100 and 200 may be entirely interconnected through a wired interface, or at least some of them may be wirelessly connected through the communication unit 110 .
  • the control unit 120 and the communication unit 110 are connected by wire, and the control unit 120 and the first unit (eg, 130 , 140 ) are connected to the communication unit 110 through the communication unit 110 . It can be connected wirelessly.
  • each element, component, unit/unit, and/or module within the wireless device 100 , 200 may further include one or more elements.
  • the controller 120 may be configured with one or more processor sets.
  • control unit 120 may be configured as a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like.
  • memory unit 130 may include random access memory (RAM), dynamic RAM (DRAM), read only memory (ROM), flash memory, volatile memory, and non-volatile memory. volatile memory) and/or a combination thereof.
  • FIG. 27 will be described in more detail with reference to the drawings.
  • the portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), and a portable computer (eg, a laptop computer).
  • a mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).
  • MS mobile station
  • UT user terminal
  • MSS mobile subscriber station
  • SS subscriber station
  • AMS advanced mobile station
  • WT wireless terminal
  • 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 a part of the communication unit 110 .
  • Blocks 110 to 130/140a to 140c respectively correspond to blocks 110 to 130/140 of FIG. 27 .
  • the communication unit 110 may transmit and receive signals (eg, data, control signals, etc.) with other wireless devices and base stations.
  • the controller 120 may perform various operations by controlling the components of the portable device 100 .
  • the controller 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 . Also, 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 a connection between the portable device 100 and other external devices.
  • the interface unit 140b may include various ports (eg, an audio input/output port and a video input/output port) for connection with an external device.
  • 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.
  • the input/output unit 140c obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 130 . can be saved.
  • the communication unit 110 may convert the information/signal stored in the memory into a wireless signal, and transmit the converted wireless signal directly to another wireless device or to a base station. Also, after receiving a radio signal from another radio device or base station, the communication unit 110 may restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 130 , it may be output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 140c.
  • various forms eg, text, voice, image, video, haptic
  • the vehicle or autonomous driving vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, and the like.
  • AV aerial vehicle
  • the vehicle or autonomous driving 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 autonomous driving. It may include a part 140d.
  • the antenna unit 108 may be configured as a part of the communication unit 110 .
  • Blocks 110/130/140a-140d correspond to blocks 110/130/140 of FIG. 27, respectively.
  • the communication unit 110 may transmit/receive signals (eg, data, control signals, etc.) to and from external devices such as other vehicles, base stations (e.g., base stations, roadside units, etc.), servers, and the like.
  • the controller 120 may control elements of the vehicle or the autonomous driving vehicle 100 to perform various operations.
  • the controller 120 may include an Electronic Control Unit (ECU).
  • the driving unit 140a may cause the vehicle or the autonomous driving vehicle 100 to run 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 the autonomous driving vehicle 100 , and may include a wired/wireless charging circuit, a battery, and the like.
  • the sensor unit 140c may obtain vehicle status, surrounding environment information, user information, and the like.
  • the sensor unit 140c includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, and a vehicle forward movement.
  • IMU inertial measurement unit
  • a collision sensor a wheel sensor
  • a speed sensor a speed sensor
  • an inclination sensor a weight sensor
  • a heading sensor a position module
  • a vehicle forward movement / may include a reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, and the like.
  • the autonomous driving unit 140d includes a technology for maintaining a driving lane, a technology for automatically adjusting speed such as adaptive cruise control, a technology for automatically driving along a predetermined route, and a technology for automatically setting a route when a destination is set. technology can be implemented.
  • the communication unit 110 may receive map data, traffic information data, and the like from an external server.
  • the autonomous driving unit 140d may generate an autonomous driving route and a driving plan based on the acquired data.
  • the controller 120 may control the driving unit 140a to move the vehicle or the autonomous driving vehicle 100 along the autonomous driving path (eg, speed/direction adjustment) according to the driving plan.
  • the communication unit 110 may obtain the latest traffic information data from an external server non/periodically, and may acquire surrounding traffic information data from surrounding vehicles.
  • the sensor unit 140c may acquire vehicle state and surrounding environment information.
  • the autonomous driving unit 140d may update the autonomous driving route and driving plan based on the newly acquired data/information.
  • the communication unit 110 may transmit information about a vehicle location, an autonomous driving route, a driving plan, and the like to an external server.
  • the external server may predict traffic information data in advance using AI technology or the like based on information collected from the vehicle or autonomous vehicles, and may provide the predicted traffic information data to the vehicle or autonomous vehicles.
  • the vehicle 30 illustrates a vehicle to which the present invention is applied.
  • the vehicle may also 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 correspond to blocks 110 to 130/140 of FIG. 27 , respectively.
  • the communication unit 110 may transmit and receive signals (eg, data, control signals, etc.) with other vehicles or external devices such as a base station.
  • the controller 120 may control components of the vehicle 100 to perform various operations.
  • 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 position measuring unit 140b may acquire position 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, location information with a surrounding vehicle, and the like.
  • the position measuring unit 140b may include a GPS and various sensors.
  • the communication unit 110 of the vehicle 100 may receive map information, traffic information, and the like from an external server and store it in the memory unit 130 .
  • the position measuring unit 140b may acquire vehicle position information through GPS and various sensors and store it in the memory unit 130 .
  • the controller 120 may generate a virtual object based on map information, traffic information, vehicle location information, and the like, and the input/output unit 140a may display the created virtual object on a window inside the vehicle ( 1410 and 1420 ). Also, the controller 120 may determine whether the vehicle 100 is normally operating within the driving line based on the vehicle location information. When the vehicle 100 deviates from the driving line abnormally, the controller 120 may display a warning on the windshield of the vehicle through the input/output unit 140a.
  • control unit 120 may broadcast a warning message regarding driving abnormality to surrounding vehicles through the communication unit 110 .
  • control unit 120 may transmit the location information of the vehicle and information on driving/vehicle abnormality to a related organization through the communication unit 110 .
  • the XR device may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a smart phone, 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 smart phone
  • 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 correspond to blocks 110 to 130/140 of FIG. 27 , respectively.
  • the communication unit 110 may transmit/receive signals (eg, media data, control signals, etc.) to/from external devices such as other wireless devices, portable devices, or media servers.
  • Media data may include images, images, and sounds.
  • the controller 120 may perform various operations by controlling the 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, and the like from the outside, and may output the generated 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 an XR device state, 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. have.
  • 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 memory unit 130 of the XR device 100a may include information (eg, data, etc.) necessary for generating an XR object (eg, AR/VR/MR object).
  • the input/output unit 140a may obtain a command to operate the XR device 100a from the user, and the controller 120 may drive the XR device 100a according to the user's driving command. For example, when the user intends to watch a movie or news through the XR device 100a, the controller 120 transmits the content request information to another device (eg, the mobile device 100b) through the communication unit 130 or can be sent to the media server.
  • another device eg, the mobile device 100b
  • the communication unit 130 may download/stream contents such as movies and news from another device (eg, the portable device 100b) or a media server to the memory unit 130 .
  • the controller 120 controls and/or performs procedures such as video/image acquisition, (video/image) encoding, and metadata generation/processing for the content, and is acquired through the input/output unit 140a/sensor unit 140b It is possible to generate/output an XR object based on information about one surrounding space or a real object.
  • 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 portable device 100b may operate as a controller for the XR device 100a.
  • the XR device 100a may obtain 3D location information of the portable device 100b, and then generate and output an XR object corresponding to the portable device 100b.
  • Robots can be classified into industrial, medical, home, military, etc. 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 correspond to blocks 110 to 130/140 of FIG. 27 , respectively.
  • the communication unit 110 may transmit/receive signals (eg, driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers.
  • the controller 120 may perform various operations by controlling the 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 may output 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, surrounding environment information, user information, and the like of the robot 100 .
  • the sensor unit 140b may include a proximity sensor, an illumination 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 travel on the ground or fly in the air.
  • the driving unit 140c may include an actuator, a motor, a wheel, a brake, a propeller, and the like.
  • AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented in any possible device or the like.
  • the AI device 100 includes a communication unit 110 , a control unit 120 , a memory unit 130 , input/output units 140a/140b , a learning processor unit 140c and a sensor unit 140d). may include Blocks 110 to 130/140a to 140d correspond to blocks 110 to 130/140 of FIG. 27, respectively.
  • the communication unit 110 uses wired/wireless communication technology to communicate with other AI devices (eg, FIGS. 24, 100x, 200, 400) or external devices such as the AI server 200 and wired/wireless signals (eg, sensor information, user input, learning). models, control signals, etc.). To this end, the communication unit 110 may transmit information in the memory unit 130 to an external device or transmit a signal received from the external device to the memory unit 130 .
  • AI devices eg, FIGS. 24, 100x, 200, 400
  • wired/wireless signals eg, sensor information, user input, learning). models, control signals, etc.
  • the communication unit 110 may transmit information in the memory unit 130 to an external device or transmit a signal received from the external device to the memory unit 130 .
  • the controller 120 may determine at least one executable 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 control the components of the AI device 100 to perform the determined operation. For example, the control unit 120 may request, search, receive, or utilize the data of the learning processor unit 140c or the memory unit 130 , and may be predicted or preferred among at least one executable operation. Components of the AI device 100 may be controlled to execute the operation. In addition, the control unit 120 collects history information including user feedback on the operation contents or operation of the AI device 100 and stores it in the memory unit 130 or the learning processor unit 140c, or the AI server ( 24 and 400) may be transmitted to an external device. The collected historical information may be used to update the learning model.
  • the memory unit 130 may store data supporting various functions of the AI device 100 .
  • the memory unit 130 may store data obtained from the input unit 140a , data obtained from the communication unit 110 , output data of the learning processor unit 140c , and data obtained from the sensing unit 140 .
  • the memory unit 130 may store control information and/or software codes necessary for the operation/execution of the control unit 120 .
  • the input unit 140a may acquire various types of data from the outside of the AI device 100 .
  • the input unit 120 may obtain training data for model learning, input data to which the learning model is applied, and the like.
  • the input unit 140a may include a camera, a microphone, and/or a user input unit.
  • the output unit 140b may generate an output related to sight, hearing, or touch.
  • the output unit 140b may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit 140 may obtain at least one of internal information of the AI device 100 , surrounding environment information of the AI device 100 , and user information by using various sensors.
  • the sensing unit 140 may include a proximity sensor, an illumination 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. have.
  • the learning processor unit 140c may train a model composed of an artificial neural network by using the training data.
  • the learning processor unit 140c may perform AI processing together with the learning processor unit of the AI server ( FIGS. 24 and 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 . Also, 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 .
  • Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof.
  • an 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), a processor, a controller, a microcontroller, a microprocessor, and the like.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • an embodiment of the present invention may be implemented in the form of modules, procedures, functions, etc. that perform the functions or operations described above.
  • the software code may be stored in the memory and driven by the processor.
  • the memory may be located inside or outside the processor, and may transmit/receive data to and from the processor by various well-known means.

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Abstract

La présente spécification concerne un procédé d'émission ou de réception d'un signal avec un codeur automatique dans un système de communication sans fil. Plus spécifiquement, le procédé mis en œuvre par un terminal de réception comprend les étapes consistant à : recevoir un premier poids d'un réseau neuronal de codeur de terminal d'émission à partir d'un terminal d'émission ; apprendre le premier poids et un second poids d'un réseau neuronal de codeur de terminal de réception sur la base d'un procédé de descente de gradient ; transmettre des informations de poids de mise à jour actualisées pour le premier poids au terminal d'émission sur la base de l'apprentissage ; et recevoir, en provenance du terminal d'émission, un signal auquel les informations de poids mises à jour sont appliquées.
PCT/KR2020/008753 2020-07-03 2020-07-03 Procédé d'émission ou de réception de signal avec un codeur automatique dans un système de communication sans fil et appareil associé WO2022004927A1 (fr)

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PCT/KR2020/008753 WO2022004927A1 (fr) 2020-07-03 2020-07-03 Procédé d'émission ou de réception de signal avec un codeur automatique dans un système de communication sans fil et appareil associé

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