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WO2023035736A1 - Procédé et appareil de précodage de système d'antenne sur la base de deux échelles de temps et d'un apprentissage profond - Google Patents

Procédé et appareil de précodage de système d'antenne sur la base de deux échelles de temps et d'un apprentissage profond Download PDF

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WO2023035736A1
WO2023035736A1 PCT/CN2022/102833 CN2022102833W WO2023035736A1 WO 2023035736 A1 WO2023035736 A1 WO 2023035736A1 CN 2022102833 W CN2022102833 W CN 2022102833W WO 2023035736 A1 WO2023035736 A1 WO 2023035736A1
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dnn
scale
time
precoding
matrix
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Chinese (zh)
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胡棋昱
蔡云龙
康凯
李旻
赵民建
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Zhejiang University ZJU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present application relates to the technical field of wireless communication, and in particular to a method and device for antenna system precoding based on dual time scales and deep learning.
  • 5G communication network With the development of wireless networks, wireless data services grow explosively. In order to meet the ensuing challenges, the new generation of 5G communication network needs to provide larger bandwidth, higher spectral efficiency, and accommodate more users.
  • the rise of 5G networks is accompanied by a significant increase in the number of users and the amount of data they transmit.
  • mmWave communication is considered as one of the key technologies to meet the high data rate transmission requirements in 5G wireless network due to its huge bandwidth.
  • the shorter wavelength of the millimeter wave allows the system to deploy a sufficient number of array antennas, among which the massive MIMO system can provide a large enough array gain for spatial multiplexing, thereby increasing system capacity and alleviating the shortage of radio spectrum.
  • the massive MIMO system needs to be precoded in the application.
  • traditional all-digital precoding needs to configure a radio frequency link for each antenna, which is costly and consumes a lot of energy.
  • hybrid analog-digital precoding is usually used, that is, a large number of antennas are connected to fewer radio frequency links through a phase shifter.
  • channel estimation and channel feedback are two important issues in hybrid precoding design.
  • Channel feedback schemes are mainly divided into two categories: (1) utilizing the space-time correlation of channel state information to reduce feedback overhead; (2) feedback schemes based on codebooks.
  • This application aims to solve one of the technical problems in the related art at least to a certain extent.
  • the present application proposes an antenna system precoding method, device, electronic device and storage medium based on dual time scales and deep learning.
  • the embodiment of the first aspect of the present application proposes an antenna system precoding method based on dual time scales and deep learning, including the following steps:
  • a long-time-scale deep neural network DNN and a short-time-scale deep neural network DNN are constructed, wherein the long-time-scale DNN and the short-time-scale DNN respectively include multiple sub-networks corresponding to the transceivers of the massive millimeter-wave MIMO system.
  • the multiple sub-networks include: a channel estimation sub-network and a channel feedback sub-network at the receiving end, and a pilot design sub-network and a hybrid precoding sub-network at the sending end;
  • the method further includes: dividing the time axis into multiple superframes according to channel statistical characteristics, and dividing each superframe into a first preset number of frames, each Each of the frames includes a second preset number of time slots, the long time scale is determined according to the superframe, and the short time scale is determined according to the time slots.
  • training the long-time-scale DNN and the short-time-scale DNN includes: alternately training the long-time-scale DNN and the short-time-scale DNN according to a dual-time-scale frame structure.
  • Time-scale DNN wherein the digital precoding matrix of the short-time-scale DNN is updated based on the low-dimensional equivalent channel in every time slot except the last time slot of each frame, and the long-time-scale DNN
  • the analog precoding matrix and the digital precoding matrix of are updated based on the high-dimensional equivalent channel at the last time slot of each frame.
  • constructing the channel feedback sub-network according to the output of binary neurons in the deep neural network, the training of the long-term scale DNN and the short-time scale DNN also includes : the pilot information is set as the training parameters of the pilot design sub-network, and the target training parameters of the pilot design sub-network are learned by stochastic gradient descent; the gradient of the binary neuron is approximated by the estimator of the sigmoid function, and passed Stochastic gradient descent trains the channel feedback subnetwork.
  • the high-dimensional pilot is estimated by the following formula:
  • high-dimensional channel feedback is performed by the following formula:
  • q is the feedback bit
  • the vectorized result of is a vector
  • the real part and the imaginary part are separated, is the training parameter of the long-time scale DNN
  • ⁇ r is the nonlinear activation function of the rth layer of the long-time scale DNN
  • sgn( ) is the activation function of the binary layer of the long-time scale DNN.
  • the hybrid precoding subnetwork includes an analog transmitter precoding module, a digital transmitter precoding module, an analog receiver precode module, a digital receiver precode module, and a demodulation module , the analog precoding is performed according to the high-dimensional original channel, including:
  • the real part and the imaginary part of the high-dimensional original channel matrix are respectively input to the analog transmitter precoding module and the analog receiver precoder module to output the analog encoder phases of the transmitter and the receiver;
  • Constrained complex vectors; the complex vectors satisfying the constant modulus constraints are converted by the following formula to generate an analog precoding matrix:
  • F RF is the analog precoding matrix of the transmitting end
  • W RF is the analog precoding matrix of the receiving end
  • N t is the number of antennas at the transmitter
  • N r is the number of antennas at the receiver.
  • the training data includes high-dimensional original channel matrix samples, Gaussian noise and data labels to be sent
  • the training of the long-term DNN further includes: moving average Or the way of sliding window to obtain high-dimensional original channel matrix samples, the moving average is the current output of the analog transmitter precoding module and the analog receiver precoding module of the long-term scale DNN and the last moment closest to the current The output is weighted average.
  • the embodiment of the second aspect of the present application proposes an antenna system precoding device based on dual time scales and deep learning, including the following modules:
  • a plurality of subnetworks, the plurality of subnetworks include: a channel estimation subnetwork and a channel feedback subnetwork at the receiving end, and a pilot design subnetwork and a hybrid precoding subnetwork at the sending end;
  • a training module configured to obtain training data with different signal-to-noise ratios, and train the long-time scale DNN and the short-time scale DNN through the training data to optimize network parameters;
  • the acquisition module is used to acquire the signal to be transmitted, perform high-dimensional pilot estimation and high-dimensional channel feedback through the long-term scale DNN completed through training, so as to restore the high-dimensional original channel matrix, and complete the short-term DNN through training Scale DNN performs low-dimensional pilot estimation and low-dimensional channel feedback to obtain low-dimensional equivalent channel matrix;
  • a coding module configured to perform analog precoding and digital precoding according to the high-dimensional original channel matrix through the long-time scale DNN, and perform digital precoding through the short-time scale DNN according to the low-dimensional equivalent channel matrix , to complete the signal transmission.
  • the embodiment of the third aspect of the present application proposes an electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, the above first The steps in the antenna system precoding method based on dual time scales and deep learning shown in the aspect.
  • the embodiment of the fourth aspect of the present application proposes a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the above-mentioned An antenna system precoding method based on dual time scales and deep learning is shown in one aspect.
  • the present application performs hybrid precoding based on dual time scales, wherein the analog precoding of the long time scale is obtained based on the channel statistical characteristics, and the digital precoding of the short time scale is obtained according to the low Dimensional real-time equivalent channel matrix is optimized, which can reduce signaling overhead and improve the robustness to channel mismatch caused by transmission delay.
  • the method jointly designs each module in the communication system through a deep learning framework to achieve end-to-end performance optimization. Statistical properties do not require precise channel mathematical models, and the calculation of deep neural networks can be parallelized, which improves the communication performance of massive MIMO systems and reduces the computational complexity of hybrid precoding.
  • the present application only needs to estimate the low-dimensional equivalent channel matrix most of the time in practical applications, so that the channel error caused by the transmission delay can be greatly reduced.
  • FIG. 1 is a schematic flowchart of an antenna system precoding method based on dual time scales and deep learning proposed in an embodiment of the present application;
  • FIG. 2 is a schematic structural diagram of a hybrid precoding millimeter-wave MIMO system proposed in an embodiment of the present application
  • FIG. 3 is a communication schematic diagram of a hybrid precoding millimeter-wave MIMO system proposed in an embodiment of the present application
  • FIG. 4 is a schematic structural diagram of a dual-time-scale frame proposed in an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of an end-to-end dual-time-scale DNN for a millimeter-wave MIMO system proposed in an embodiment of the present application;
  • FIG. 6 is a schematic diagram of a hybrid precoding design framework and its data transmission proposed by the embodiment of the present application.
  • FIG. 7 is a schematic diagram of a sliding window with a cache capacity of 3 proposed by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a frame structure of a centralized training DNN proposed in an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a frame structure of a distributed training DNN proposed in an embodiment of the present application.
  • FIG. 10 is a schematic diagram of a comparison of the bit error rates of a dual-time-scale DNN proposed in the embodiment of the present application and a traditional solution under different signal-to-noise ratios;
  • FIG. 11 is a schematic diagram of a comparison of the bit error rate of a dual-time-scale DNN proposed in the embodiment of the present application and a traditional solution under different feedback bit numbers;
  • FIG. 12 is a schematic diagram of a comparison of bit error rates of a dual-time-scale DNN proposed in the embodiment of the present application and a traditional solution under different pilot lengths;
  • FIG. 13 is a schematic diagram of a communication process of an end-to-end dual-scale millimeter-wave MIMO system proposed in an embodiment of the present application;
  • FIG. 14 is a schematic structural diagram of an antenna system precoding device based on dual time scales and deep learning proposed by an embodiment of the present application.
  • the existing dual-time-scale algorithms have high computational complexity, and cannot jointly design each module in the communication system.
  • optimization algorithms can be used in related technologies, there are still problems such as high computational complexity, impossibility of real-time application, precise mathematical modeling of problems, poor robustness against environmental changes, and differences between communication modules. design issues.
  • this application uses deep learning technology to carry out joint design of each module in a massive MIMO system. Compared with optimization algorithms, deep learning has lower computational complexity, does not require precise mathematical modeling of problems, and is robust against errors.
  • the communication modules can be jointly designed, and the end-to-end deep learning framework is suitable for the joint design of each module in this system.
  • the deep learning framework can jointly design all modules to achieve end-to-end performance optimization.
  • the deep learning framework implicitly learns the statistical properties of the channel in a data-driven manner during the process of optimizing an end-to-end communication system, without requiring an accurate mathematical model of the channel.
  • the calculation of DNN can be parallelized, and the computational complexity is much lower than that of traditional optimization algorithms.
  • Figure 1 is a schematic flowchart of a method for precoding an antenna system based on dual time scales and deep learning proposed in the embodiment of the present application. As shown in Figure 1, the method includes the following steps:
  • Step 101 constructing a long-time-scale deep neural network DNN and a short-time-scale deep neural network DNN, wherein the long-time-scale DNN and the short-time-scale DNN respectively include a plurality of sub-transceivers corresponding to a large-scale millimeter-wave multiple-input multiple-output MIMO system
  • the multiple subnetworks include: a channel estimation subnetwork and a channel feedback subnetwork at the receiving end, and a pilot design subnetwork and a hybrid precoding subnetwork at the sending end.
  • the application first constructs a deep neural network (DNN for short), and divides the DNN into two parts based on a dual time scale, that is, constructing a long-term scale deep neural network DNN and a short-time scale deep neural network DNN.
  • the long-term DNN is used for high-dimensional pilot estimation and high-dimensional original channel feedback, and the analog precoding and digital precoding are optimized according to the recovered high-dimensional channel, and the short-time-scale DNN is used for low-dimensional pilot estimation and The low-dimensional equivalent channel is fed back, and the digital precoding is optimized according to the recovered low-dimensional channel.
  • each of the long-time-scale DNN and the short-time-scale DNN encapsulates all modules of a transceiver of a large-scale millimeter wave multiple-in multiple-out (MIMO for short) system, that is, the long-time scale DNN
  • the short-time-scale DNN includes sub-networks equivalent to channel estimation and channel feedback at the receiving end of MIMO systems, as well as pilot design and hybrid precoding sub-networks at the sending end. That is to say, the hybrid precoding millimeter-wave MIMO system after deploying the DNN of this application performs channel estimation, channel quantization, and channel feedback based on DNN at the receiving end, and performs pilot design and hybrid precoding optimization at the transmitting end.
  • the received pilot signal is mapped to feedback bits at the receiving end, and then the received feedback bits are mapped to a hybrid precoding matrix at the sending end.
  • the transmitting end of the system is equipped with N t transmitting antennas and RF links, sending N s data streams to the receiving end, where,
  • the receiving end (could be the user end) is equipped with N r receiving antennas and RF links, where, At the transmit end, the RF link is connected to a network of phase shifters that will digital output signals into N t encoded analog signals.
  • N r receiving antennas are connected to a network of phase shifters and radio frequency link.
  • the sender transmits N s parallel data It consists of 0-1 bits of N s ⁇ log 2 M dimensions. Then, these data are mapped into symbols according to the M-dimensional modulation scheme The symbolic vector s satisfies These symbols are first digitally sent pre-encoded processing, and then simulated pre-encoding
  • F RF represents the analog precoding matrix that can only adjust the phase and is realized through the phase shifter network, so it needs to meet the constant modulus constraint
  • the digital precoding matrix F BB needs to be normalized by power So that the power constraint of the sending end is satisfied, where PT represents the maximum transmission power.
  • the received signal needs to be analog encoded at the receiving end and digital code processing.
  • the detected signal can be written as where W RF needs to satisfy the constant modulus constraint
  • the detected signal r is demodulated to recover the N s data streams, yielding recovered bits
  • the communication process of the system includes channel estimation, channel feedback and hybrid precoding.
  • the base station is used as the sending end, and the sending end needs to obtain the channel matrix H to perform hybrid precoding. Therefore, before transmitting data, it is necessary to send pilots to estimate the channel.
  • the sending end first sends a pilot matrix of length L Then the pilot signal received by the receiver in, and denote the analog transmit pilot and the analog receive pilot, respectively, whose columns are selected from the discrete Fourier transform (DFT) matrix to satisfy the constant modulus constraint, Represents the Gaussian white noise matrix,
  • DFT discrete Fourier transform
  • the sending end sends the pilots in the pilot matrix sequentially according to the time sequence, and sends the lth transmission of the pilot matrix ( The lth column, in the embodiment of the present application, the number of transmissions corresponds to the number of columns of the pilot matrix) needs to meet the power constraint
  • the signal received by the receiver from The channel H is estimated, and useful information is extracted from it, such as angle of arrival, channel gain, etc., and then the information is compressed into B bits and fed back to the sending end Among them, B is the amount of bits preset according to actual needs, and the mapping Indicates a feedback scheme.
  • this application proposes a dual-time-scale scheme to simultaneously consider the high-dimensional original channel and channel statistical properties.
  • the time axis is divided into a plurality of superframes according to channel statistical characteristics, and each superframe is divided into a first preset number of frames, each frame includes a second preset number of time slots,
  • the long time scale is determined in terms of superframes, and the short time scale is determined in terms of time slots.
  • the channel statistical characteristics are constant during this period.
  • the superframe is composed of T f frames, and each frame is divided into T s time slots.
  • the first preset number is T f
  • the second preset number is T s .
  • High-dimensional The original channel remains unchanged within each slot.
  • the long-term scale in the embodiment of the present application is that the statistical properties of the channel are fixed in each superframe, a superframe contains T f frames, and the short-time scale high-dimensional original channel H It is assumed to be fixed at each time slot.
  • the receiving end can obtain a complete high-dimensional original channel matrix sample in each frame, and can obtain a real-time low-dimensional equivalent channel matrix Heq in each time slot, analog and digital precoding
  • the matrices need to be optimized at different time scales based on H and Heq respectively.
  • the long-term scale analog precoding matrix ⁇ F RF , W RF ⁇ is updated at the last moment of each frame based on the estimated high-dimensional original channel matrix H to achieve multi-antenna array gain.
  • the short-time-scale digital precoding matrix ⁇ F BB , W BB ⁇ is updated based on the estimated low-dimensional equivalent channel matrix Heq in each time slot to achieve spatial multiplexing gain, while the short-time-scale digital precoding matrix
  • ⁇ F RF , W RF ⁇ is fixed. Therefore, when the hybrid precoding matrix is updated by the dual-time-scale method, it is only necessary to estimate the low-dimensional equivalent channel matrix most of the time, so that the channel error caused by the transmission delay can be greatly reduced.
  • Step 102 acquiring training data with different signal-to-noise ratios, and using the training data to train the long-time-scale DNN and the short-time-scale DNN, so as to optimize network parameters.
  • the purpose of offline training of the long-time scale DNN and the short-time scale DNN is to obtain the training parameters of each sub-network in the DNN through training data with different signal-to-noise ratios, and optimize the performance of the DNN through the obtained training parameters.
  • Network parameters which are convenient for subsequent predictions by fixing and optimizing network parameters in practical applications.
  • the training data with different signal-to-noise ratios includes training samples ⁇ H,n,S b ⁇ , that is, high-dimensional original channel matrix samples, Gaussian noise, and data labels to be sent.
  • training data can be obtained in different ways.
  • the historical data can be obtained according to the actual application scenario of the system, and the historical data generated during the communication of the massive millimeter-wave MIMO system pre-stored in the database can be used as the training data.
  • the channel of the scale millimeter-wave MIMO system can be modeled, and training data can be obtained in real time according to the established channel model.
  • a narrowband millimeter wave channel model is established, the narrowband millimeter wave channel model includes N cl clusters, and each cluster includes N ray propagation paths. Each path includes the sending and receiving directions of the channel (sending angle, arrival angle), and path complex gain.
  • the channel matrix is expressed as:
  • d and ⁇ represent the distance between adjacent antennas and the wavelength of the carrier, respectively.
  • the training parameters can be iteratively updated through stochastic gradient descent (SGD) with binary cross-entropy (BCE) as the target loss function.
  • SGD stochastic gradient descent
  • BCE binary cross-entropy
  • the binary cross entropy can be expressed by the following formula:
  • S b represents the transmission symbol matrix, which consists of 0-1 bits with a dimension of N s ⁇ log 2 M.
  • maximizing BCE is equivalent to maximizing the attainable rate.
  • bit-error rate (BER) of the training set can be expressed as:
  • the long-time-scale DNN and the short-time-scale DNN can be alternately trained according to the frame structure of the dual-time scale , where the digital precoding matrix of the short-time scale DNN is updated based on the low-dimensional equivalent channel in each frame except the last slot, and the analog precoding matrix and digital precoding matrix of the long-time scale DNN The matrix is updated based on the high-dimensional equivalent channel at the last slot of each frame.
  • a short-time-scale DNN is trained for the first T s ⁇ 1 slots of each frame, with the input ⁇ H,F RF ,W RF ,n,S b ⁇ , where the simulated precoding matrix ⁇ F RF ,W RF ⁇ is computed by a long-time scale DNN.
  • the long-term scale DNN is trained, and the input is ⁇ H,n,S b ⁇ , that is, the long-time scale DNN is trained once in each frame, and the short-time scale DNN is trained once in each time slot. The two are trained alternately until convergence.
  • the principle of DNN output hybrid precoding is the same as above.
  • the pilot In the first T s -1 time slots of each frame, the pilot The short-time scale DNN outputs a digital precoding matrix ⁇ F BB , W BB ⁇ . In the last slot of each frame, the pilot is sent The long-term scale DNN outputs a mixed precoding (including digital and analog) matrix ⁇ F BB , F RF , W BB , W RF ⁇ .
  • each training phase in DNN in combination with Figure 5 and Figure 6, including: pilot training phase, channel feedback training stage and hybrid precoding design training stage.
  • the training carried out in each training stage can be regarded as the training of the corresponding sub-network.
  • the pilot training stage the pilot design sub-network is trained
  • the channel feedback training stage is the channel estimation sub-network. Network and channel feedback sub-network for training and so on.
  • the end-to-end dual-time-scale DNN for mmWave MIMO systems includes a long-time scale DNN10 and a short-time scale DNN20.
  • DC-NN and DP-NN can Shared learning parameters.
  • the receiver needs to estimate the low-dimensional equivalent channel matrix H eq in the first T s -1 slots of each frame, and estimate the high-dimensional original channel matrix H in the last slot of each frame .
  • the sending end when performing pilot training on long-term scale DNN, in order to estimate the high-dimensional original channel matrix H, the sending end sends training pilot And the analog precoding matrix (called the analog pilot matrix in the channel estimation stage) Where L represents the pilot length. Then, the received pilot signal matrix goes through the simulated receiving matrix Expressed as in, Represents a Gaussian noise matrix. That is, when the pilot training is performed on the long-term scale DNN in the embodiment of the present application, the input and output of the long-time scale DNN are H and Among them, H is the acquired training data.
  • the parameter set is selected in the embodiment of this application instead of the parameters in the DNN network as training parameters, that is, in this application, the above-mentioned pilot information is set as the pilot design subnetwork training parameters.
  • the Gaussian pilot used in this application and Waiting for the pilot selected from the DFT matrix the pilot parameters obtained by training It can achieve better channel estimation performance and better adapt to channel statistical characteristics.
  • each element of the two matrices is set as a training parameter, and after each training is completed, it is divided by its own modulus
  • the embodiment of the present application also scales make in is the pilot frequency of the lth transmission, that is, the matrix column l of .
  • the sender When performing pilot training on a short-time scale DNN, in order to estimate the low-dimensional equivalent channel matrix Heq , the sender sends the training pilot matrix Receiver receives in is a Gaussian noise matrix.
  • the input and output of the short-time scale DNN in this application are Heq and Among them, Heq is the acquired training data.
  • the application sets the training parameters as In the channel estimation stage, unlike the long-time-scale DNN, the analog encoder ⁇ F RF , W RF ⁇ of the short-time-scale DNN is not obtained through training, but is the same as the analog encoder used in the data transmission stage of the previous frame. That is, the short time scale DNN estimates the equivalent channel
  • the analog encoder ⁇ F RF ,W RF ⁇ is part of the equivalent channel, and this application also scales Such that the pilot satisfies the power constraint.
  • the receiving end feeds back the low-dimensional equivalent channel matrix Heq after quantization, and in the last time slot of each frame, the receiving end feeds back The high-dimensional original channel matrix H after quantization.
  • the receiving end when performing channel feedback training on the long-term scale DNN, in the last time slot of each frame, first, the receiving end based on the received pilot signal matrix Estimate the high-dimensional original channel matrix H. Then, the receiving end extracts useful information from it and quantizes it into B bits to feed back to the sending end for subsequent hybrid precoding design.
  • the vectorization result of DNN the input of DNN is a vector Representation with real and imaginary parts separated Indicates the training parameters, and ⁇ r represents the nonlinear activation function of the rth layer.
  • the sign function sgn( ⁇ ) is the activation function of the last layer (binary layer), which is used to generate the feedback bit vector q (each element of q takes the value of 0 or 1).
  • the feedback process of the low-dimensional equivalent channel matrix Heq is similar to the feedback process of the above-mentioned long-time-scale DNN. Specifically, in the first T s -1 time slots of each frame, the receiving end first bases on the received pilot signal matrix Estimate the low-dimensional equivalent channel matrix Heq , extract useful information from it, quantize it into Beq bits and feed it back to the transmitter for subsequent digital precoding design. These two steps can be implemented with a fully connected DNN at the Req layer, that is, the feedback bits at the receiving end can be expressed by the following formula:
  • the vectorization result of DNN the input of DNN is a vector Representation with real and imaginary parts separated Represents the training parameters of DNN, and the sign function sgn( ) is the activation function of the last layer (binary layer), which is used to generate the feedback bit vector (Each element of q eq has a value of 0 or 1). Therefore, for the discrete output of the binary neuron, the gradient of the binary neuron is approximated by the estimator of the sigmoid function, which is convenient for the subsequent stochastic gradient descent training channel Feedback sub-networks, enabling gradient-based training.
  • DNN Since the dimension of the feedback vector q eq is much smaller than the dimension of q, because the dimension of He eq is much smaller than the dimension of H, therefore, in the embodiment of the present application, DNN with fewer layers and parameters can be used to obtain q eq .
  • this application uses a short time scale DNN based on q eq to update the digital precoding matrix ⁇ F BB , W BB ⁇ , in At the last slot of each frame, the digital and analog precoding matrices ⁇ F RF , F BB , W RF , W BB ⁇ are updated using a long-term scale DNN based on q.
  • the sender when performing hybrid precoding design training on long-term scale DNN, in the last time slot of each frame, the sender receives the feedback bit q to restore the high-dimensional original channel matrix Then, based on the recovered Design hybrid precoding matrix ⁇ F RF , F BB , W RF , W BB ⁇ with DNN.
  • the DNN includes 5 sub-networks, the analog precoder network (analog precoder NN, AP-NN), the digital transmitter precoder network (digital precoder NN, DP-NN), and the analog receiver precoder network. Coding network (analog combiner NN, AC-NN), digital receiver precoding network (digital combiner NN, DC-NN), and demodulation network.
  • the recovered channel matrix is first The real part and imaginary part are stored separately to become a real number matrix. Then, the real part and imaginary part are input into AP-NN and AC-NN respectively, and the analog encoders at the sending end and receiving end are respectively output through AP-NN and AC-NN. phase and From this, the complex vector that satisfies the constant modulus constraint can be calculated by the following formula:
  • the simulated precoding matrix is generated by the following formula:
  • N t is the number of antennas at the transmitting end
  • N r is the number of antennas at the receiving end.
  • the equivalent channel matrix The real part and imaginary part are stored separately and become a real matrix, which is input into DP-NN and DC-NN, and output to the digital encoders at the sending end and receiving end respectively (Real and imaginary parts are stored separately).
  • the digital precoding matrix can be generated by the following formula: Then perform power normalization by the following formula to obtain the final digital precoding matrix, while ensuring that the power constraints are satisfied:
  • the sender when performing hybrid precoding design training on short-time-scale DNN, in the first T s -1 time slots of each frame, the sender receives feedback bits q eq , which are used to restore the low-dimensional equivalent channel matrix Then, based on the recovered channel matrix The sending end designs the digital precoding matrix ⁇ F BB , W BB ⁇ , and the analog precoding matrix ⁇ F RF , W RF ⁇ is fixed at this time (directly use the last time slot of the previous frame, the simulation obtained by the long-term scale DNN precoding matrix).
  • the short-time-scale DNN includes DP-NN and DC-NN, which are used to generate digital precoding matrices F BB and W BB at the transmitter and receiver respectively.
  • the principle of generating the matrix can refer to the above-mentioned long-term scale The steps of DNN performing hybrid precoding design training will not be repeated here.
  • the DNN signal flow is shown in the signal flow chart in Figure 5.
  • the whole process simulates the sending signal S b through the mixed precoding ⁇ F RF , F BB ⁇ at the sending end, channel fading H, noise n, receiving end Hybrid precoding ⁇ W RF , W BB ⁇ , the process of recovering the signal at the receiving end.
  • the training sample ⁇ H,n,S b ⁇ is input into DNN to generate a mixed precoding matrix, and finally the received signal r is obtained.
  • the real part and imaginary part of the received signal r are separated, r is converted into a real-valued vector, and input to the demodulation network to generate a restored signal Furthermore, according to S b and The training is completed by minimizing the end-to-end binary cross entropy as described above, and iteratively updating the training parameters of the DNN through stochastic gradient descent.
  • the training parameters of each sub-network in the DNN are obtained, and the optimized training parameters are fixed to facilitate subsequent prediction
  • the stage makes predictions based on determined network parameters.
  • Step 103 obtain the signal to be transmitted, perform high-dimensional pilot estimation and high-dimensional channel feedback through the trained long-term DNN to restore the high-dimensional original channel matrix, and perform low-dimensional pilot through the trained short-time scale DNN Frequency estimation and low-dimensional channel feedback to obtain low-dimensional equivalent channel matrix.
  • Step 104 Perform analog precoding and digital precoding according to the high-dimensional original channel matrix through the long-time scale DNN, and perform digital precoding according to the low-dimensional equivalent channel matrix through the short-time scale DNN to complete signal transmission.
  • the signal to be transmitted is the parallel data actually to be sent by the sending end.
  • the implementation method in the above prediction stage can be referred to, and the high-dimensional pilot estimation and high-dimensional channel feedback can be performed through the trained long-term scale DNN to restore the high-dimensional original channel matrix, and the training can be completed
  • the short-time scale DNN of performs low-dimensional pilot estimation and low-dimensional channel feedback to obtain a low-dimensional equivalent channel matrix.
  • analog precoding and digital precoding are performed according to the high-dimensional original channel matrix by a long-time scale DNN, and digital precoding is performed according to a low-dimensional equivalent channel matrix by a short-time scale DNN.
  • the received pilot signal is mapped to feedback bits at the receiving end, and then the received feedback bits are mapped to a hybrid precoding matrix at the sending end.
  • the specific implementation principle and implementation process can refer to the above prediction stage
  • the scheme in the above-mentioned training phase is not repeated here, that is, after the fixed and optimized training parameters and the design of the hybrid precoder matrix are obtained through the above training phase scheme, in the prediction phase, each module in the signal flow shown in Figure 6 is
  • the optimized parameters are substituted into the formulas in the above embodiments for channel estimation, channel feedback and hybrid precoding design, so that the trained After the network performs channel estimation, channel feedback and hybrid precoding design, the transmitted signal can be accurately recovered at the receiving end.
  • the DNN with discrete output 0-1 variables is trained. Since the binary layer (the output is 0 or 1, the sign function sgn(x) is used as the activation function The derivative of ) is almost 0 everywhere, and it is not derivable at the origin.
  • the existing backpropagation method cannot be directly used to train the layer before the binary layer. Therefore, in one embodiment of the present application, in the process of gradient backpropagation, a smooth everywhere-differentiable function is used to approximate the sign function.
  • a stepwise approximation method is also adopted in the embodiment of the present application, that is, as the training process progresses, the slope of the substitution function is slowly increased, so that the substitution function gradually approaches the sign function. Therefore, in the initial stage of training, the DNN training can achieve relevant effects relatively easily and quickly, just like ordinary DNNs. After the network training reaches the preset effect, the slope of the substitution function is increased to avoid numerical instability and make the training more stable and converge faster.
  • the expression of the replacement function is:
  • ⁇ (i) is the parameter of the i-th epoch, which needs to satisfy ⁇ (i) ⁇ ⁇ (i-1) .
  • high-dimensional original channel matrix samples can be obtained by means of a sliding average or a sliding window.
  • the sliding average is a long-term scale
  • the current output of the analog transmitter precoding module and the analog receiver precoding module of the DNN are weighted averaged with the output at the last moment closest to the current one.
  • the long-term scale variable (analog precoder) ⁇ F RF , W RF ⁇ needs to adapt to the statistical characteristics of the channel. Therefore, the optimization of long-term scale variables needs to be based on enough high-dimensional original channel samples H, however, since only one sample H can be obtained for each frame. For this reason, in the embodiment of this application, the channel sample H is fully utilized by using the sliding average method, that is, a weighted average is made between the current output result of the DNN and the result at the previous moment, and the weighted average is performed by the following formula:
  • the embodiment of this application proposes to use a sliding window with a certain buffer capacity D
  • D the matrix input to the AC-NN and AP-NN networks in the tth frame is from the t-D+1th frame to the tth frame All channel samples, i.e.
  • the sliding window buffer capacity shown in FIG. 7 is 3, and in some embodiments of the present application, the buffer capacity may also be determined according to actual needs.
  • the dual-time-scale DNN training methods proposed in the embodiment of the present application can be divided into two types: centralized and distributed, and any of them can be used for training.
  • this application also designs corresponding frame structures for centralized and distributed training to complete the training.
  • Figure 8 shows the frame structure design of centralized training.
  • DNN Before DNN can be officially deployed, it needs to be trained offline. After the training is completed, the DNN corresponding to the receiving end (including structure and parameters) needs to be distributed to the user end.
  • a frame includes several time slots, and the structure of a time slot consists of the following four parts: indication bits, pilot symbols, feedback bits, and transmission data.
  • the indication bit represents whether the current time slot uses a long-time-scale DNN or a short-time-scale DNN, whether the current channel statistical characteristics have changed, and whether the channel change speed has changed.
  • the frame and time slot lengths need to be adaptively adjusted. When the channel changes faster, the frame and time slot lengths need to be shortened to obtain more high-dimensional original channel samples to track channel changes.
  • Figure 9 shows the frame structure design of distributed training.
  • the difference from the frame structure of centralized training is that before DNN is officially used and deployed, it needs to be distributed offline training. This process includes the interaction of DNN input and output, gradient information, etc. between the base station (transmitter) and the user (receiver). . After the training is completed, it can be directly deployed and used without the process of distributing DNN (including structure and parameters) to the client.
  • this application performs hybrid precoding based on dual time scales, and jointly designs each module in the communication system through a deep learning framework to achieve end-to-end performance optimization.
  • the following is combined with the actual application of the antenna system precoding method based on dual time scales and deep learning of the present application and the precoding scheme in the prior art
  • the test results are compared, among which, Figure 10 compares the bit error rate of the dual-time-scale DNN and the traditional scheme under different signal-to-noise ratios, and Figure 11 compares the bit-error rate of the dual-time-scale DNN and the traditional scheme under different feedback bit numbers, Fig.
  • the dual-time-scale DNN of the present application can significantly reduce the channel feedback overhead and the pilot length, while maintaining a good bit error rate performance.
  • the antenna system precoding method based on dual time scales and deep learning in the embodiment of the present application performs hybrid precoding based on dual time scales, in which the long-term analog precoding is obtained based on channel statistical characteristics, and the short-time scale
  • the digital precoding of is optimized according to the low-dimensional real-time equivalent channel matrix, which can reduce signaling overhead and improve the robustness to channel mismatch caused by transmission delay.
  • the method jointly designs each module in the communication system through a deep learning framework to achieve end-to-end performance optimization. Statistical characteristics, no precise channel mathematical model is required, and the calculation of deep neural network can be parallelized, which improves the communication performance of massive MIMO system, reduces the computational complexity of hybrid precoding, and improves the bit error rate performance.
  • the transmitting end in the first T s -1 time slots of each frame, sends the pilot matrix
  • the receiving end estimates the low-dimensional equivalent channel matrix Heq according to the received pilot signal, quantizes it, and feeds back the quantized channel information q eq to the sending end.
  • the sender restores the low-dimensional equivalent channel matrix according to the feedback result
  • data transmission is performed according to the signal flow shown in FIG. 6 .
  • the sender In the last slot of each frame, the sender first sends training pilots and simulated pilots The receiving end restores the high-dimensional original channel H according to the received pilot signal, quantizes it, and feeds back the quantized result q bits to the sending end. Then, the sender restores the output according to the feedback result And design a hybrid precoder ⁇ F BB , F RF , W BB , W RF ⁇ . Finally transmit the actual data s to be sent. Since the dimension of Heq is much smaller than H, the dimension of feedback information qeq is much smaller than that of q.
  • the MIMO system based on dual time scale hybrid precoding in this application can also be extended to other different types of systems to adapt to different application scenarios needs.
  • the network in the present application is extended to the TDD system through fewer steps, wherein only the channel feedback part in the MIMO system in the above embodiment needs to be omitted, and the channel can be fully utilized Reciprocity does.
  • the base station sends the pilot
  • the user receives the pilot and estimates the downlink channel, quantizes the channel and feeds it back to the base station, and the base station restores
  • the downlink channel is used for precoding, but in TDD, only the following modifications are required: remove the channel feedback part, change the base station transmission pilot to the user transmission pilot, and the other network structures remain unchanged. Then the corresponding specific process is.
  • the user sends the pilot frequency, the base station estimates the uplink channel, and according to the channel reciprocity, can directly obtain the downlink channel, and directly perform precoding according to the downlink channel.
  • the dual-time scale hybrid precoding-based MIMO system of the present application can also be simply extended to the OFDM system.
  • the channels of the extended OFDM system are equivalent to the 1024 channels (channels that each subcarrier has) in the system described in the above-mentioned embodiments, then these The channel is input as sample data into the long-time scale deep neural network DNN and short-time scale deep neural network DNN of this application for training, that is, if the OFDM to be extended contains 1024 subcarriers, then repeat the above steps 101 to 104,
  • the extended OFDM system can be obtained by repeating 1024 times.
  • the present application also proposes an antenna system precoding device based on dual time scales and deep learning.
  • FIG. 14 is a schematic structural diagram of an antenna system precoding device based on dual time scales and deep learning proposed by an embodiment of the present application. As shown in FIG. 14 , the device includes a construction module 100 , a training module 200 , an acquisition module 300 and an encoding module 400 .
  • the building block 100 is used to construct a long-time-scale deep neural network DNN and a short-time-scale deep neural network DNN, wherein the long-time-scale DNN and the short-time-scale DNN respectively include transceivers with a large-scale millimeter-wave multiple-input multiple-output MIMO system
  • Multiple sub-networks corresponding to the machine, the multiple sub-networks include: the channel estimation sub-network and the channel feedback sub-network at the receiving end, and the pilot design sub-network and hybrid precoding sub-network at the sending end.
  • the training module 200 is used to obtain training data with different signal-to-noise ratios, and use the training data to train the long-time-scale DNN and the short-time-scale DNN, so as to optimize network parameters.
  • the acquisition module 300 is used to acquire the signal to be transmitted, and perform high-dimensional pilot estimation and high-dimensional channel feedback through the trained long-term DNN to restore the high-dimensional original channel matrix, and perform high-dimensional pilot estimation and high-dimensional channel feedback through the trained short-time scale DNN. Low-dimensional pilot estimation and low-dimensional channel feedback to obtain low-dimensional equivalent channel matrix.
  • the coding module 400 is used to perform analog precoding and digital precoding according to the high-dimensional original channel matrix through the long-time scale DNN, and perform digital precoding according to the low-dimensional equivalent channel matrix through the short-time scale DNN to complete signal transmission.
  • the construction module 100 is further configured to divide the time axis into multiple superframes according to the channel statistical characteristics, and divide each superframe into a first preset number of frames, and each frame includes a second preset Given a number of time slots, the long time scale is determined in terms of superframes, and the short time scale is determined in terms of time slots.
  • the training module 200 is also used to alternately train the long-time-scale DNN and the short-time-scale DNN according to the dual-time-scale frame structure, wherein the digital precoding matrix of the short-time-scale DNN is divided in each frame Each time slot other than the last time slot is updated based on the low-dimensional equivalent channel, and the analog precoding matrix and digital precoding matrix of the long-term DNN are in the last time slot of each frame, based on the high-dimensional equivalent channel to update.
  • the training module 200 is also used to set the pilot information as the training parameters of the pilot design sub-network, and learn the target training parameters of the pilot design sub-network through stochastic gradient descent; through the estimator of the sigmoid function Approximate gradients of binary neurons and train channel feedback subnetworks via stochastic gradient descent.
  • the training module 200 is also used to perform high-dimensional pilot estimation by the following formula:
  • the training module 200 is also used to perform high-dimensional channel feedback through the following formula:
  • q is the feedback bit
  • the vectorized result of is a vector
  • the real part and the imaginary part are separated, is the training parameter of the long-time scale DNN
  • ⁇ r is the nonlinear activation function of the rth layer of the long-time scale DNN
  • sgn( ) is the activation function of the binary layer of the long-time scale DNN.
  • the hybrid precoding sub-network includes an analog transmitting end precoding module, a digital transmitting end precoding module, an analog receiving end precoding module, a digital receiving end precoding module and a demodulation module
  • the encoding module 400 is also used to: input the real part and the imaginary part of the high-dimensional original channel matrix to the precoding module of the analog sending end and the precoding module of the analog receiving end respectively, so as to output the analog encoder phases of the sending end and the receiving end;
  • the complex number vector of the modulus constraint; the complex number vector satisfying the constant modulus constraint is transformed by the following formula to generate the analog precoding matrix:
  • F RF is the analog precoding matrix of the transmitting end
  • W RF is the analog precoding matrix of the receiving end
  • Represents the operation of converting a vector into a matrix is the phase of the analog encoder at the transmitter, is the analog encoder phase at the receiving end, N t is, N r is.
  • the training module 200 is also used to acquire high-dimensional original channel matrix samples by means of sliding average or sliding window.
  • the current output of the encoding module and the output at the last moment closest to the current are weighted averaged.
  • the antenna system precoding device based on dual time scales and deep learning in the embodiment of the present application performs hybrid precoding based on dual time scales, in which the long-term analog precoding is obtained based on channel statistical characteristics, and the short-time scale
  • the digital precoding of is optimized according to the low-dimensional real-time equivalent channel matrix, which can reduce signaling overhead and improve the robustness to channel mismatch caused by transmission delay.
  • the device jointly designs each module in the communication system through a deep learning framework to achieve end-to-end performance optimization.
  • the deep learning framework implicitly learns the channel performance in a data-driven manner during the process of optimizing the end-to-end communication system.
  • Statistical characteristics no precise channel mathematical model is required, and the calculation of deep neural network can be parallelized, which improves the communication performance of massive MIMO system, reduces the computational complexity of hybrid precoding, and improves the bit error rate performance.
  • the present disclosure also proposes an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, the present disclosure based on Steps in an antenna system precoding method with dual time scales and deep learning.
  • the present disclosure also proposes a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements a An Antenna System Precoding Method Based on Dual Time Scales and Deep Learning.
  • the terminal device may include, but not limited to, a processor and a memory.
  • the processor can be a central processing unit (Central Processing Unit, referred to as CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, referred to as DSP), application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC) ), off-the-shelf programmable gate array (Field-Programmable Gate Array, referred to as FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the memory can be used to store the computer programs and/or modules, and the processor implements the terminal by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory various functions of the device.
  • the electronic device of the present disclosure can implement the steps in the foregoing method embodiments when the processor executes the computer program.
  • the processor executes the computer program
  • the functions of the modules/units in the above device embodiments are implemented.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device.
  • computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. processing to obtain the program electronically and store it in computer memory.
  • each part of the present application may be realized by hardware, software, firmware or a combination thereof.
  • various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system.
  • a suitable instruction execution system For example, if implemented in hardware as in another embodiment, it can be implemented by any one or a combination of the following techniques known in the art: a discrete Logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGA), Field Programmable Gate Arrays (FPGA), etc.
  • each functional unit in each embodiment of the present application may be integrated into one processing module, each unit may exist separately physically, or two or more units may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.
  • the storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, and the like.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features.
  • the features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise specifically defined.

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Abstract

L'invention concerne un procédé et un appareil de précodage de système d'antenne basés sur deux échelles de temps et un apprentissage profond. Le procédé comprend les étapes consistant : à construire un réseau neuronal profond (DNN) à grande échelle de temps et un DNN à courte échelle de temps, les DNN construits comprenant chacun une pluralité de sous-réseaux correspondant à un émetteur-récepteur d'un système à entrées multiples-sorties multiples à ondes millimétriques à grande échelle ; à entraîner les DNN construits au moyen de données d'entraînement ; à acquérir un signal à transmettre, à réaliser une estimation de fréquence pilote à haute dimension et une rétroaction de canal à haute dimension au moyen d'un DNN à grande échelle de temps formé à long terme, et à réaliser une estimation de fréquence pilote à faible dimension et une rétroaction de canal à faible dimension au moyen du DNN à courte échelle de temps ; et à réaliser un précodage analogique et un précodage numérique selon une matrice de canal d'origine à haute dimension et au moyen du DNN à grande échelle de temps, et à réaliser un précodage numérique selon une matrice de canal équivalente à faible dimension et au moyen du DNN à courte échelle de temps. Au moyen du procédé, les surcharges de signalisation peuvent être réduites, la robustesse d'un système peut être améliorée, et les modules dans le système sont conçus conjointement, ce qui permet de réduire la complexité de calcul.
PCT/CN2022/102833 2021-09-09 2022-06-30 Procédé et appareil de précodage de système d'antenne sur la base de deux échelles de temps et d'un apprentissage profond Ceased WO2023035736A1 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116760666A (zh) * 2023-07-06 2023-09-15 亚信科技（南京）有限公司 信道估计方法、装置、电子设备及存储介质
CN118337274A (zh) * 2024-06-17 2024-07-12 南京控维通信科技有限公司 一种适用于卫星通信的高鲁棒性通信方法
CN119420388A (zh) * 2025-01-06 2025-02-11 中国人民解放军火箭军工程大学 一种用于毫米波信道系统的sub-6GHz信道辅助的混合预编码方法
CN119544414A (zh) * 2024-11-13 2025-02-28 南京航空航天大学 一种融合信道统计特性的无监督mimo信道估计方法与系统
CN119946329A (zh) * 2025-04-07 2025-05-06 中国科学技术大学 视频流媒体双时间尺度无线传输方法、系统及存储介质

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113972939B (zh) * 2021-09-09 2022-07-12 浙江大学 基于双时间尺度和深度学习的天线系统预编码方法和装置
CN114553274B (zh) * 2022-01-27 2023-03-31 北京理工大学 基于对抗学习的安全自预编码机优化方法
CN114696878B (zh) * 2022-02-23 2023-10-17 浙江大学 一种基于混合预编码和信道估计的联合设计方法和装置
CN114553643B (zh) * 2022-04-24 2022-08-02 杭州电子科技大学 双时间尺度协同感知的毫米波智能超表面信道估计方法
EP4344143A1 (fr) 2022-09-22 2024-03-27 Nokia Solutions and Networks Oy Matrice de préambule
CN120741963B (zh) * 2025-09-03 2025-10-31 合肥瀚博智能科技有限公司 一种多通道天线线源自动化测试方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107332598A (zh) * 2017-06-26 2017-11-07 浙江理工大学 一种基于深度学习的mimo系统联合预编码和天线选择方法
CN110099016A (zh) * 2019-05-14 2019-08-06 东南大学 一种基于深度学习网络的毫米波稀疏阵面信道估计方法
WO2020092391A1 (fr) * 2018-10-29 2020-05-07 Board Of Regents, The University Of Texas System Récepteurs ofdm à basse résolution par apprentissage profond
WO2020188316A1 (fr) * 2019-03-15 2020-09-24 Telefonaktiebolaget Lm Ericsson (Publ) Précodage de réseau neutre artificiel pour systèmes mimo massifs
CN113162666A (zh) * 2021-04-19 2021-07-23 北京科技大学 一种面向智慧钢铁的大规模mimo混合预编码方法及装置
CN113972939A (zh) * 2021-09-09 2022-01-25 浙江大学 基于双时间尺度和深度学习的天线系统预编码方法和装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109743268B (zh) * 2018-12-06 2022-02-15 东南大学 基于深度神经网络的毫米波信道估计和压缩方法
CN109921882B (zh) * 2019-02-20 2021-08-27 深圳市宝链人工智能科技有限公司 一种基于深度学习的mimo解码方法、装置及存储介质
US11005697B2 (en) * 2019-09-03 2021-05-11 Cisco Technology, Inc. Orthogonal frequency-division multiplexing equalization using deep neural network
CN110855585B (zh) * 2019-09-30 2020-11-10 北京交通大学 一种毫米波大规模mimo系统的信道估计方法
CN112737985B (zh) * 2020-12-25 2023-04-07 东南大学 基于深度学习的大规模mimo信道联合估计和反馈方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107332598A (zh) * 2017-06-26 2017-11-07 浙江理工大学 一种基于深度学习的mimo系统联合预编码和天线选择方法
WO2020092391A1 (fr) * 2018-10-29 2020-05-07 Board Of Regents, The University Of Texas System Récepteurs ofdm à basse résolution par apprentissage profond
WO2020188316A1 (fr) * 2019-03-15 2020-09-24 Telefonaktiebolaget Lm Ericsson (Publ) Précodage de réseau neutre artificiel pour systèmes mimo massifs
CN110099016A (zh) * 2019-05-14 2019-08-06 东南大学 一种基于深度学习网络的毫米波稀疏阵面信道估计方法
CN113162666A (zh) * 2021-04-19 2021-07-23 北京科技大学 一种面向智慧钢铁的大规模mimo混合预编码方法及装置
CN113972939A (zh) * 2021-09-09 2022-01-25 浙江大学 基于双时间尺度和深度学习的天线系统预编码方法和装置

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CN119420388A (zh) * 2025-01-06 2025-02-11 中国人民解放军火箭军工程大学 一种用于毫米波信道系统的sub-6GHz信道辅助的混合预编码方法
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