WO2024002003A1 - Procédé de détermination de modèle de rétroaction de canal, terminal, et dispositif côté réseau - Google Patents

Procédé de détermination de modèle de rétroaction de canal, terminal, et dispositif côté réseau Download PDF

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Publication number
WO2024002003A1
WO2024002003A1 PCT/CN2023/102375 CN2023102375W WO2024002003A1 WO 2024002003 A1 WO2024002003 A1 WO 2024002003A1 CN 2023102375 W CN2023102375 W CN 2023102375W WO 2024002003 A1 WO2024002003 A1 WO 2024002003A1
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WIPO (PCT)
Prior art keywords
model
channel
information
target
terminal
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PCT/CN2023/102375
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English (en)
Chinese (zh)
Inventor
李刚
韩双锋
温子睿
石子烨
Original Assignee
中国移动通信有限公司研究院
中国移动通信集团有限公司
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Publication of WO2024002003A1 publication Critical patent/WO2024002003A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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
    • H04B7/0417—Feedback systems
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station

Definitions

  • the present disclosure relates to the field of communications, and in particular, to a method for determining a channel feedback model, a terminal, and a network side device.
  • the channel feedback model is generally divided into two parts: an encoder and a decoder.
  • an encoder needs to be deployed on the terminal side and a decoder needs to be deployed on the network side.
  • the terminal uses the encoder to compress the estimated channel state information (Channel State Information, CSI) into a series of bit streams.
  • CSI Channel State Information
  • the bit streams are fed back through the uplink
  • the channel is sent to the network side, and the network side finally recovers the original CSI based on the bit stream.
  • the training of the channel feedback model is deployed on the network side. Due to the need to adapt to different terminals, the network side needs to collect a large amount of channel data to train the channel feedback model, resulting in high overhead on the network side.
  • Embodiments of the present disclosure provide a method for determining a channel feedback model, a terminal, and a network side device to solve the problem that the network side needs to collect a large amount of channel data to train the channel feedback model, resulting in high overhead on the network side.
  • embodiments of the present disclosure provide a method for determining a channel feedback model, which is applied to a terminal.
  • the method includes:
  • the model information includes at least one of the following:
  • performing model training on the target channel feedback model based on the channel information includes:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weights of the encoding model in the target channel feedback model are maintained.
  • the coefficients and weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the method also includes:
  • the model training of the target channel feedback model based on the channel information includes:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the method before performing model training on the target channel feedback model based on the channel information, the method further includes:
  • a target channel feedback model is selected based on the channel information.
  • selecting a target channel feedback model based on the channel information includes:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • embodiments of the present disclosure provide a method for determining a channel feedback model, which is applied to network side equipment.
  • the method includes:
  • Receive model information of the decoding model in the target channel feedback model sent by the terminal wherein the target channel feedback model is obtained by the terminal performing model training based on the channel information, or the target channel feedback model is the The terminal selects based on the channel information.
  • the model information includes at least one of the following:
  • the method before receiving the model information of the decoding model in the target channel feedback model sent by the terminal, the method further includes:
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the method Before sending the training configuration for model training to the terminal based on the channel information, the method further includes:
  • the first channel characteristics are compared with the second channel characteristics, and a training configuration for model training is determined based on the comparison results.
  • the second channel characteristics are average channel characteristics determined based on the training samples.
  • the first channel characteristics include at least one of the following:
  • an embodiment of the present disclosure provides a terminal, where the terminal includes:
  • a receiving module configured to receive the channel state information reference signal sent by the network side device, and perform channel measurement on the channel state information reference signal to obtain channel information;
  • a processing module configured to perform model training on a target channel feedback model based on the channel information, or select a target channel feedback model based on the channel information;
  • a first sending module configured to send the information in the target channel feedback model to the network side device. Decode model information for the model.
  • the model information includes at least one of the following:
  • processing module is specifically used to:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weights of the encoding model in the target channel feedback model are maintained.
  • the coefficients and weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the device also includes:
  • a second sending module configured to send the channel information to the network side device, and receive the training configuration for model training sent by the network side device based on the channel information
  • the processing module is specifically used for:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the device also includes:
  • a selection module configured to select a target channel feedback model based on the channel information.
  • processing module is specifically used to:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • embodiments of the present disclosure provide a network side device, where the network side device includes:
  • the first sending module is configured to send a channel state information reference signal to the terminal, so that the terminal performs channel measurement on the channel state information reference signal to obtain channel information;
  • a receiving module configured to receive model information of a decoding model in a target channel feedback model sent by the terminal, where the target channel feedback model is obtained by the terminal through model training based on the channel information, or the target channel The feedback model is selected for the terminal based on the channel information.
  • the model information includes at least one of the following:
  • the device also includes:
  • a second sending module configured to receive the channel information sent by the terminal, and send training configuration for model training to the terminal based on the channel information
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the device also includes:
  • An acquisition module configured to acquire first channel characteristics based on the channel information
  • Determining module configured to compare the first channel characteristic with the second channel characteristic, and determine the training configuration for model training based on the comparison result, the second channel characteristic is an average value determined based on the training sample Channel characteristics.
  • the first channel characteristics include at least one of the following:
  • embodiments of the present disclosure provide a terminal, including a transceiver and a processor,
  • the transceiver is configured to receive a channel state information reference signal sent by a network side device, and perform channel measurement on the channel state information reference signal to obtain channel information;
  • the processor is configured to perform model training on a target channel feedback model based on the channel information, or select a target channel feedback model based on the channel information;
  • the transceiver is further configured to send model information of the decoding model in the target channel feedback model to the network side device.
  • the model information includes at least one of the following:
  • the processor is used for:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the target channel is maintained
  • the weight coefficients of the encoding model in the feedback model and the weight coefficients of other layer neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the transceiver is further configured to: send the channel information to the network side device, and receive a training configuration for model training sent by the network side device based on the channel information;
  • the processor is also used to:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the processor is further configured to select a target channel feedback model based on the channel information.
  • the processor is also used to:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • embodiments of the present disclosure provide a network side device, including a transceiver and a processor,
  • the transceiver is configured to send a channel state information reference signal to a terminal, so that the terminal performs channel measurement on the channel state information reference signal to obtain channel information;
  • the transceiver is further configured to receive model information of the decoding model in the target channel feedback model sent by the terminal, wherein the target channel feedback model is obtained by the terminal through model training based on the channel information, or the The target channel feedback model is selected by the terminal based on the channel information.
  • the model information includes at least one of the following:
  • the transceiver is also used for:
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the processor is used for:
  • the first channel characteristics are compared with the second channel characteristics, and a training configuration for model training is determined based on the comparison results.
  • the second channel characteristics are average channel characteristics determined based on the training samples.
  • the first channel characteristics include at least one of the following:
  • an embodiment of the present disclosure provides a terminal, including: a processor, a memory, and a program stored on the memory and executable on the processor.
  • the program is executed by the processor, the above is implemented. The steps of the method for determining the channel feedback model described in the first aspect.
  • an embodiment of the present disclosure provides a network-side device, including: a processor, a memory, and a program stored on the memory and executable on the processor.
  • a program stored on the memory and executable on the processor.
  • embodiments of the present disclosure provide a computer-readable storage medium.
  • a computer program is stored on the computer-readable storage medium.
  • the computer program is executed by a processor, the channel feedback model described in the first aspect is implemented.
  • the steps of the determination method; or when the computer program is executed by the processor, the steps of the channel feedback model determination method described in the second aspect are implemented.
  • the terminal receives the channel state information reference signal sent by the network side device, performs channel measurement on the channel state information reference signal to obtain channel information; performs model training on the target channel feedback model based on the channel information, or Select a target channel feedback model based on the channel information; and send model information of the decoding model in the target channel feedback model to the network side device.
  • the terminal performs model training on the target channel feedback model or selecting the target channel feedback model, the overhead on the network side can be reduced.
  • Figure 1 is one of the flow charts of a method for determining a channel feedback model provided by an embodiment of the present disclosure
  • Figure 2 is one of the structural schematic diagrams of a channel feedback model provided by an embodiment of the present disclosure
  • Figure 3 is the second flow chart of a method for determining a channel feedback model provided by an embodiment of the present disclosure
  • Figure 4 is a schematic structural diagram of model information provided by an embodiment of the present disclosure.
  • Figure 5 is a schematic diagram of a model training result provided by an embodiment of the present disclosure.
  • Figure 6 is a schematic classification diagram of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 7a is one of the schematic diagrams of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 7b is a second schematic diagram of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 7c is a third schematic diagram of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 7d is a fourth schematic diagram of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 7e is a fifth schematic diagram of a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 8 is the third flowchart of a method for determining a channel feedback model provided by an embodiment of the present disclosure
  • Figure 9 is the fourth flowchart of a method for determining a channel feedback model provided by an embodiment of the present disclosure.
  • Figure 10 is the fifth flowchart of a method for determining a channel feedback model provided by an embodiment of the present disclosure
  • Figure 11 is one of the structural schematic diagrams of a terminal provided by an embodiment of the present disclosure.
  • Figure 12 is one of the structural schematic diagrams of a network side device provided by an embodiment of the present disclosure.
  • Figure 13 is a second structural schematic diagram of a terminal provided by an embodiment of the present disclosure.
  • Figure 14 is a second structural schematic diagram of a network side device provided by an embodiment of the present disclosure.
  • a method for determining a channel feedback model, a terminal and a network side device are proposed to solve the problem that the network side needs to collect a large amount of channel data to train the channel feedback model, resulting in high overhead on the network side.
  • Figure 1 is a flow chart of a method for determining a channel feedback model provided by an embodiment of the present disclosure.
  • Process diagram, used for terminals, as shown in Figure 1, the method includes the following steps:
  • Step 101 Receive the channel state information reference signal sent by the network side device, and perform channel measurement on the channel state information reference signal to obtain channel information.
  • the channel state information reference signal (CSI-RS) is sent by the network side device and is used by the terminal to perform channel measurements and obtain channel information.
  • the channel information may be channel state information (Channel State Information, CSI).
  • Step 102 Perform model training on a target channel feedback model based on the channel information, or select a target channel feedback model based on the channel information.
  • the target channel feedback model may include a coding model and a decoding model.
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the target channel feedback model is maintained The weight coefficients of the encoding model and the weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged.
  • the target layer neural network is at least one layer of neural networks of the decoding model. .
  • the target channel feedback model can be trained in the following two ways:
  • the terminal determines the training configuration for online training.
  • the terminal can determine the optimal training configuration and the number of model layers and weight coefficients that need to be fed back to the network side device.
  • the terminal can start training from the last layer of the decoding model until there is no significant improvement in performance.
  • the terminal can train the penultimate layer according to priority 1, train the penultimate layer and penultimate layer according to priority 2, and train the penultimate layer, penultimate layer and penultimate layer according to priority 3.
  • ... train the penultimate layer, the penultimate layer, the penultimate layer, ..., the penultimate m layer according to the priority m, and stop training until the training accuracy does not increase.
  • priority 1 is the highest priority.
  • the network side device determines the training configuration for online training.
  • Network-side devices can configure optimal training configurations for terminals based on historical big data analysis results.
  • the model structure can be divided into a frozen layer and a fine-tuning layer.
  • the terminal does not update the weight coefficients during back propagation during model training.
  • the terminal performs gradient descent during back propagation during model training. weight system Number of updates.
  • the terminal only needs to feedback the weight coefficients of the fine-tuning layer, which can reduce training overhead and feedback overhead.
  • the training configuration is as follows: fine-tuning layer: 1, 2...m; freezing layer: m...n; number of training iterations.
  • Step 103 Send model information of the decoding model in the target channel feedback model to the network side device.
  • the terminal may send model information of the decoding model in the target channel feedback model after model training to the network side device.
  • the terminal when the terminal uses the channel information obtained by itself to perform online training on the target channel feedback model, it can obtain the uplink transmission resources through the uplink service resource transmission request, and then in the service data adaptation protocol (The model information of the decoding model in the target channel feedback model is transmitted in the Service Data Adaptation Protocol (SDAP) data packet.
  • SDAP Service Data Adaptation Protocol
  • the model information can include: model identifier (Identifier, ID); model layer that needs to be updated. numerical information (for example, L1 to Ln); model weight coefficients that need to be updated.
  • the uplink service resource transmission request may include: the terminal sends Scheduling Request (SR) signaling to the Radio Access Network (Radio Access Network, RAN) through the Physical Uplink Control Channel (PUCCH), and the RAN
  • SR Scheduling Request
  • the physical downlink control channel Physical downlink control channel, PDCCH
  • DCI Downlink Control Information
  • PDCCH Physical downlink control channel
  • DCI Downlink Control Information
  • UL uplink
  • MAC Media Access Control
  • the MAC control unit Control Element, CE
  • BSR Buffer Status Report
  • the network side equipment (such as the base station) can periodically send CSI-RS; after receiving the CSI-RS, the terminal performs channel estimation, uses the coding model in the target channel feedback model to perform compression and quantization, and then sends the CSI-RS to the network.
  • the side device sends the compressed and quantized channel information; the network side device receives the compressed and quantized channel information, uses the updated decoding model to decompress the channel information, and obtains complete downlink channel estimation information.
  • the encoding model and decoding model can be trained uniformly in an end-to-end manner during model training, and can also be used in pairs during deployment; if mismatched encoding models and decoding models are used, it will cause a significant decrease in channel information recovery accuracy.
  • model training is performed by a network-side device. Yes, since model training requires a lot of computing power, network-side equipment needs to collect a large amount of channel data, which is expensive. Moreover, limited by the collection scenarios and collection scale of training data, and due to the changeable channel environment where the terminal is located, it is difficult for a fixed channel feedback model to achieve good performance in all wireless scenarios, that is, there is a general problem chemical issues.
  • the terminal performs model training on the target channel feedback model, or selects the target channel feedback model based on channel information, which can solve the problem of reduced recovery accuracy caused by the generalization of the encoding model and the decoding model in different environments. question.
  • the terminal receives the channel state information reference signal sent by the network side device, performs channel measurement on the channel state information reference signal to obtain channel information; performs model training on the target channel feedback model based on the channel information, or Select a target channel feedback model based on the channel information; and send model information of the decoding model in the target channel feedback model to the network side device.
  • the terminal performs model training on the target channel feedback model or selecting the target channel feedback model, the overhead on the network side can be reduced.
  • the model information includes at least one of the following:
  • the terminal sends the model identifier of the decoding model to the network side device, and/or all the weight coefficients of the decoding model or part of the weight coefficients of the decoding model, so that the network side device can based on the model identifier sent by the terminal and /or the weight coefficient updates the decoding model.
  • performing model training on the target channel feedback model based on the channel information includes:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weights of the encoding model in the target channel feedback model are maintained.
  • the coefficients and weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the target channel feedback model can be obtained by the network side device using training samples to train a basic model.
  • the encoding model in the basic model is composed of four fully connected layers
  • the decoding model in the basic model is also composed of four fully connected layers.
  • the initial weight coefficient of the basic model can be obtained by training the network side device through the collected big data.
  • the terminal can choose three types.
  • the base model is retrained on channel data sets of different sizes. For example, as shown in Figure 5, the basic model can be retrained through large data sets, medium data sets, and small data sets. Moreover, different layers of the base model can be frozen to retrain the base model. As can be seen from Figure 5, there is a better training strategy for retraining on the terminal side.
  • the Normalized Mean Square Error (NMSE) of the last layer of training is higher, but the NMSE is fast after training the last two layers. decline, the subsequent marginal effects will be diminishing, and retraining the last two layers is a better choice to balance efficiency and performance.
  • the performance of the decoding model can be optimized by optimizing the weight coefficients of some layers of the decoding model. For the terminal, only feeding back part of the layer weight coefficients of the decoding model can greatly reduce the overhead of air interface transmission.
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weight coefficients of the target layer neural network in the target channel feedback model are maintained.
  • the weight coefficients of the encoding model and the weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged, so that only the weight coefficients of some layers are trained during model training, and then the network side
  • the device only transmits the weight coefficients of some layers of the decoding model, which can reduce the overhead of air interface transmission.
  • the method also includes:
  • the model training of the target channel feedback model based on the channel information includes:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the terminal can send an uplink sounding reference signal (SRS) to the network side device, and receive a training configuration for model training sent by the network side device.
  • the training configuration can be based on channel characteristics. Determined, the channel characteristics may be determined based on the channel information sent by the terminal and the SRS.
  • Wireless channel characteristics include the number of uplink and downlink multipaths, angles, multipath delays and other parameters.
  • TDD Time Division Duplex
  • wireless channels have good mutuality, and terminals can communicate with the network side.
  • the device sends the uplink sounding reference signal SRS for channel information acquisition, and the network side device measures the SRS.
  • SRS uplink sounding reference signal
  • the device sends the uplink sounding reference signal SRS for channel information acquisition, and the network side device measures the SRS.
  • Obtain the uplink channel matrix H and use the uplink and downlink reciprocity to obtain the downlink channel matrix H', and calculate the precoding matrix and beamforming vector for downlink data transmission through the downlink channel matrix H'.
  • the uplink and downlink are deployed at different frequencies, and there is no complete channel reciprocity.
  • the angle and delay of each path on the wireless channel are mainly affected by the spatial angle of the transceiver. It is determined by factors such as the relationship, the position and material of the reflector in the wireless environment, and there is no strong relationship with the frequency of the wireless signal.
  • the frequency of the wireless channel will greatly affect the path loss, penetration loss, polarization leakage factor and other components of the wireless channel, which will have a greater impact on the amplitude changes and phase changes experienced by each path.
  • the network side device can obtain partial channel characteristics of the downlink channel based on the partial reciprocity of the channel, and can also obtain the channel characteristics based on the channel information fed back by the terminal.
  • the terminal sends the channel information to the network side device, and receives the training configuration for model training sent by the network side device based on the channel information; the terminal feeds back the target channel based on the training configuration
  • the model performs model training so that the training configuration can be determined through the network side device.
  • the method before performing model training on the target channel feedback model based on the channel information, the method further includes:
  • a target channel feedback model is selected based on the channel information.
  • selecting a target channel feedback model based on the channel information includes:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • the static characteristic parameters may include but are not limited to: network configuration parameters, such as macro stations/small stations, the number of multi-antenna ports on the network side, etc.; terminal capability parameters, such as the number of terminal multi-antenna ports, terminal Artificial Intelligence (AI) Reasoning ability, etc.
  • the above static characteristic parameters generally belong to the information that needs to be reported when the terminal initially accesses the network, and remains basically unchanged during the occurrence of communication services.
  • Dynamic environment parameters can correspond to various dynamic scenarios. Dynamic environment parameters can include channel environment parameters and channel quality parameters, etc. Channel environment parameters can be channel environment parameters under line-of-sight or non-line-of-sight transmission. Number, channel quality parameters may include signal-to-noise ratio range, etc.
  • the network side device can form different training sets based on target parameters during the training stage of the basic model, train multiple models on the basis of different training sets, and divide the multiple models into different applicable scenarios. Multiple categories.
  • the terminal can select a target channel feedback model from multiple models.
  • the multiple models can be first classified according to static feature parameters to obtain multiple semi-static categories, and then determined by the static feature parameters.
  • Each subcategory continues to be classified according to dynamic environment parameters, and each subcategory can be divided into multiple dynamic subcategory models.
  • encoding models or decoding models can be shared between multiple models within each subclass.
  • the encoding model can be called an encoder, and the decoding model can be called a decoder.
  • the encoding model can be called an encoder, and the decoding model can be called a decoder.
  • five encoding model-decoding model modes are listed. Multiple encoding models can share one decoding model, or multiple decoding models can share one encoding model. The five modes listed can be divided into two categories according to whether there are multiple encoding models or decoding models to choose from, namely acde/b or abde/c.
  • Each encoding model-decoding model pair corresponds to a set of dynamic categories. In actual use, a specific group of encoding models-decoding models can be selected to compress and restore CSI.
  • selecting a target channel feedback model from at least two channel feedback models based on the target parameters may be to mark applicable static feature parameters and/or dynamic environment parameters for each pair of encoding model-decoding model, according to The target parameters select the corresponding encoding model-decoding model by looking up the dictionary.
  • At least two channel feedback models can be divided into multiple subcategories according to applicable static characteristic parameters and/or dynamic environment parameters, and a target channel feedback model is selected from the at least two channel feedback models based on the target parameters, It can be that the encoding model and decoding model of the corresponding subcategory are selected according to the target parameters by searching a dictionary, and the encoding model and decoding model in the selected subcategory are used in turn to compress and restore the channel information, and the recovery accuracy is calculated, and the subcategories are traversed. The models in the model are sorted according to the recovery accuracy, and the model with the highest recovery accuracy is selected as the target channel feedback model.
  • the terminal can notify the network side device of the decoding model of the selected target channel feedback model.
  • the notification method can be by sending signaling carrying the decoding model to the network side device.
  • the transmission of model information of the decoding model can be implemented by extending Radio Resource Control (RRC) or MAC CE signaling.
  • RRC Radio Resource Control
  • MAC CE MAC CE signaling
  • a Logic Channel Group (LCG) ID field can be added to the MAC CE signaling, and this field carries the model identification of the decoding model.
  • selecting a target channel feedback model from at least two channel feedback models based on static characteristic parameters and/or dynamic environment parameters can obtain a channel feedback model that matches the current static characteristic parameters and/or dynamic environment parameters.
  • Figure 8 is a flow chart of a method for determining a channel feedback model provided by an embodiment of the present disclosure, which is used for network side equipment. As shown in Figure 8, the method includes the following steps:
  • Step 201 Send a channel state information reference signal to the terminal, so that the terminal performs channel measurement on the channel state information reference signal to obtain channel information.
  • Step 202 Receive model information of the decoding model in the target channel feedback model sent by the terminal, where the target channel feedback model is obtained by the terminal performing model training based on the channel information, or the target channel feedback model Select for the terminal based on the channel information.
  • this embodiment is an implementation of the network side device corresponding to the embodiment shown in Figure 1.
  • the network side device corresponding to the embodiment shown in Figure 1.
  • no further details will be given in this embodiment.
  • the model information includes at least one of the following:
  • the method before receiving the model information of the decoding model in the target channel feedback model sent by the terminal, the method further includes:
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the method Before sending the training configuration for model training to the terminal based on the channel information, the method further includes:
  • the first channel characteristics are compared with the second channel characteristics, and a training configuration for model training is determined based on the comparison results.
  • the second channel characteristics are average channel characteristics determined based on the training samples.
  • the first channel characteristics are obtained based on the channel information; the first channel characteristics are compared with the second channel characteristics, and the training configuration for model training is determined based on the comparison results, and the second channel characteristics are compared with each other.
  • the channel characteristics are average channel characteristics determined based on the training samples.
  • the training configuration can be determined by comparing the average channel characteristics determined by the training samples of the training basic model and the first channel characteristics, and appropriate training configurations can be determined for different first channel characteristics, thereby achieving better model training effects. .
  • the first channel characteristics include at least one of the following:
  • the angle domain information reflects the sparsity of the beam/antenna.
  • different training configurations can be determined by whether the proportion of the first three beams with the largest path gain in the overall power gain exceeds the first threshold indicated by the angle domain information.
  • the first threshold is determined by the second channel characteristics.
  • the proportion of the first three beams with the largest path gains indicated by the angle domain information in the second channel characteristics in the overall power gain is the first threshold.
  • the first threshold can be 80%.
  • the delay domain information reflects the sparsity of the delay domain/multipath, non-line of sight (NLOS) or line of sight (Line of Sight, LOS) channels.
  • the delay domain information can be used Whether the indicated K factor (that is, the power ratio occupied by the main path) exceeds the second threshold determines different training configurations.
  • the second threshold is determined by the second channel characteristics.
  • the delay domain information in the second channel characteristics indicates The K factor of is the second threshold.
  • the second threshold may be 50%.
  • the Doppler domain information reflects the sparsity in the time domain. For example, Different training configurations are determined by whether the frequency offset power proportion of the first five main paths indicated by the Doppler domain information exceeds a third threshold.
  • the third threshold is determined by the second channel characteristics. In one embodiment, the third channel characteristics
  • the frequency offset power proportion of the first five main paths indicated by the mid-Doppler domain information is the third threshold.
  • the third threshold may be 60%.
  • the proportion of the beams with the largest gain in the first three paths indicated by the angle domain information in the overall power gain is A
  • the K factor indicated by the delay domain information is B
  • the K factor indicated by the Doppler domain information is B.
  • the frequency offset power proportion of the first five main paths is C.
  • the encoding model consists of four fully connected layers
  • the decoding model consists of four fully connected layers.
  • the model structure is divided into a frozen layer and a fine-tuning layer.
  • the terminal does not update the weight coefficients during model training and backpropagation.
  • the fine-tuning layer the terminal performs weight coefficients through gradient descent during model training and backpropagation. Coefficient update.
  • the training configuration for model training can be: the number of frozen layers is set to be larger, and the fine-tuning layer is the fourth layer of the decoding model;
  • the training configuration for model training can be: the number of frozen layers is set to a small number, and the fine-tuning layers are the third and fourth layers of the decoding model;
  • the training configuration for model training can be: a small number of consecutive samples, a sample number of 100, and a duration of 500ms;
  • the training configuration for model training can be: a large number of consecutive samples, a sample number of 1000, and a duration of 1s.
  • the terminal conducts online training on the encoding model and the decoding model to determine the channel feedback model. Specifically, as shown in Figure 9, it includes the following process:
  • the channel feedback model includes a coding model and a decoding model.
  • the base station can send the channel feedback model to the terminal, or the channel feedback model can be pre-configured in the terminal;
  • the base station sends the CSI-RS configuration through the RRC reconfiguration message
  • the base station periodically sends CSI-RS to the terminal according to the CSI-RS configuration
  • the terminal receives CSI-RS and measures CSI-RS to obtain channel state information
  • the terminal sends a CSI report to the base station and sends an SRS to the base station;
  • the base station selects the configuration for online training based on channel characteristics
  • the base station sends the online training configuration to the terminal
  • the terminal performs online training on the encoding model and decoding model based on channel state information
  • the terminal sends the model information of the trained decoding model to the base station;
  • the base station updates the decoding model based on the received model information
  • the base station periodically sends CSI-RS to the terminal;
  • the terminal uses the trained coding model to compress CSI and performs quantization processing on the compressed CSI;
  • the terminal reports the compressed and quantized CSI to the base station
  • the base station uses the updated decoding model to decode CSI.
  • the channel feedback model is determined by the terminal selecting the channel feedback model. Specifically, as shown in Figure 10, it includes the following process:
  • the channel feedback model includes a coding model and a decoding model.
  • the base station can send the channel feedback model to the terminal, or the channel feedback model can be pre-configured in the terminal;
  • the base station sends the CSI-RS configuration through the RRC reconfiguration message
  • the base station periodically sends CSI-RS to the terminal according to the CSI-RS configuration
  • the terminal receives CSI-RS and measures CSI-RS to obtain channel state information
  • the terminal selects a decoding model based on the channel state information obtained by measurement
  • the terminal sends the model information of the selected decoding model to the base station;
  • the base station updates the decoding model based on the received model information
  • the base station periodically sends CSI-RS to the terminal;
  • the terminal uses the coding model to compress CSI and performs quantization processing on the compressed CSI;
  • the terminal reports the compressed and quantized CSI to the base station
  • the base station uses the updated decoding model to decode CSI.
  • Figure 11 is a schematic structural diagram of a terminal provided by an embodiment of the present disclosure. As shown in 11, the terminal 300 includes:
  • the receiving module 301 is configured to receive the channel state information reference signal sent by the network side device, and perform channel measurement on the channel state information reference signal to obtain channel information;
  • the processing module 302 is configured to perform model training on a target channel feedback model based on the channel information, or select a target channel feedback model based on the channel information;
  • the first sending module 303 is configured to send model information of the decoding model in the target channel feedback model to the network side device.
  • the model information includes at least one of the following:
  • processing module is specifically used to:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weights of the encoding model in the target channel feedback model are maintained.
  • the coefficients and weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the device also includes:
  • a second sending module configured to send the channel information to the network side device, and receive the training configuration for model training sent by the network side device based on the channel information
  • the processing module is specifically used for:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the device also includes:
  • a selection module configured to select a target channel feedback model based on the channel information.
  • processing module is specifically used to:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • the terminal can implement each process implemented by the method embodiment shown in Figure 1 and can achieve the same beneficial effects. To avoid repetition, details will not be described here.
  • Figure 12 is a schematic structural diagram of a network side device provided by an embodiment of the present disclosure.
  • the network side device 400 includes:
  • the first sending module 401 is configured to send a channel state information reference signal to the terminal, so that the terminal performs channel measurement on the channel state information reference signal to obtain channel information;
  • the receiving module 402 is configured to receive the model information of the decoding model in the target channel feedback model sent by the terminal, wherein the target channel feedback model is obtained by the terminal through model training based on the channel information, or the target The channel feedback model is selected by the terminal based on the channel information.
  • the model information includes at least one of the following:
  • the device also includes:
  • a second sending module configured to receive the channel information sent by the terminal, and send training configuration for model training to the terminal based on the channel information
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the device also includes:
  • An acquisition module configured to acquire first channel characteristics based on the channel information
  • Determining module configured to compare the first channel characteristic with the second channel characteristic, and determine the training configuration for model training based on the comparison result, the second channel characteristic is an average value determined based on the training sample Channel characteristics.
  • the first channel characteristics include at least one of the following:
  • the network side device can implement each process implemented by the method embodiment shown in Figure 8 and can achieve the same beneficial effects. To avoid duplication, details will not be described here.
  • An embodiment of the present disclosure also provides a terminal, including: a processor, a memory and a device stored in the A program on the memory that can be run on the processor.
  • a terminal including: a processor, a memory and a device stored in the A program on the memory that can be run on the processor.
  • an embodiment of the present disclosure also provides a terminal, including a bus 501, a transceiver 502, an antenna 503, a bus interface 504, a processor 505 and a memory 506.
  • the transceiver 502 is used to receive a channel state information reference signal sent by a network side device, and perform channel measurement on the channel state information reference signal to obtain channel information;
  • the processor 505 is configured to perform model training on a target channel feedback model based on the channel information, or select a target channel feedback model based on the channel information;
  • the transceiver 502 is also configured to send model information of the decoding model in the target channel feedback model to the network side device.
  • the model information includes at least one of the following:
  • the processor 505 is used for:
  • the weight coefficients of the target layer neural network in the decoding model are trained based on the channel information, and the weights of the encoding model in the target channel feedback model are maintained.
  • the coefficients and weight coefficients of other layers of neural networks in the decoding model except the target layer neural network remain unchanged;
  • the target layer neural network is at least one layer of neural network of the decoding model.
  • the transceiver 502 is also configured to: send the channel information to the network side device, and receive a training configuration for model training sent by the network side device based on the channel information;
  • the processor 505 is also used to:
  • Model training is performed on the target channel feedback model based on the training configuration.
  • the processor 505 is also configured to select a target channel feedback model based on the channel information.
  • processor 505 is also used to:
  • target parameters include static characteristic parameters and/or dynamic environment parameters
  • a target channel feedback model is selected from at least two channel feedback models based on the target parameters.
  • bus 501 may include any number of interconnected buses and bridges, bus 501 will include one or more processors represented by processor 505 and memory represented by memory 506 various circuits linked together. Bus 501 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, etc., which are all well known in the art and therefore will not be described further herein.
  • Bus interface 504 provides an interface between bus 501 and transceiver 502.
  • Transceiver 502 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium.
  • the data processed by the processor 505 is transmitted on the wireless medium through the antenna 503. Further, the antenna 503 also receives the data and transmits the data to the processor 505.
  • Processor 505 is responsible for managing bus 501 and general processing, and may also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions.
  • Memory 506 may be used to store data used by processor 505 when performing operations.
  • the processor 505 can be a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable logic gate array (Field Programmable Gate Array, FPGA) or a complex programmable logic gate array.
  • Logic device Complex Programmable logic device, CPLD).
  • An embodiment of the present disclosure also provides a network-side device, including: a processor, a memory, and a program stored on the memory and executable on the processor.
  • a network-side device including: a processor, a memory, and a program stored on the memory and executable on the processor.
  • this embodiment of the present disclosure also provides a network side device, including a bus 601, a transceiver 602, an antenna 603, a bus interface 604, a processor 605 and a memory 606.
  • a network side device including a bus 601, a transceiver 602, an antenna 603, a bus interface 604, a processor 605 and a memory 606.
  • the transceiver 602 is used to send a channel state information reference signal to the terminal, so that the terminal performs channel measurement on the channel state information reference signal to obtain channel information;
  • the transceiver 602 is also configured to receive model information of the decoding model in the target channel feedback model sent by the terminal, wherein the target channel feedback model is obtained by the terminal through model training based on the channel information, or The target channel feedback model is selected by the terminal based on the channel information.
  • the model information includes at least one of the following:
  • the transceiver 602 is also used to:
  • the training configuration is used to perform model training on the target channel feedback model.
  • the target channel feedback model is obtained by the network side device using training samples to train a basic model
  • the processor 605 is used for:
  • the first channel characteristics are compared with the second channel characteristics, and a training configuration for model training is determined based on the comparison results.
  • the second channel characteristics are average channel characteristics determined based on the training samples.
  • the first channel characteristics include at least one of the following:
  • bus 601 may include any number of interconnected buses and bridges, bus 601 will include one or more processors 605 represented by processor 605 and memory 606 The various circuits of memory are linked together. Bus 601 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, etc., which are all well known in the art and therefore will not be described further herein.
  • Bus interface 604 provides an interface between bus 601 and transceiver 602.
  • Transceiver 602 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium.
  • the data processed by the processor 605 is transmitted on the wireless medium through the antenna 603. Further, the antenna 603 also receives the data and transmits the data to the processor 605.
  • Processor 605 is responsible for managing bus 601 and general processing, and may also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 606 May be used to store data used by processor 605 when performing operations.
  • the processor 605 can be a CPU, ASIC, FPGA or CPLD.
  • Embodiments of the present disclosure also provide a computer-readable storage medium.
  • a computer program is stored on the computer-readable storage medium.
  • the computer program is executed by a processor, each process of the above-mentioned channel feedback model determination method embodiment is implemented, and the same can be achieved. The technical effects will not be repeated here to avoid repetition.
  • the computer-readable storage medium is such as read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
  • the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk). ), includes several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present disclosure.

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

La présente divulgation se rapporte au domaine technique des communications, et concerne un procédé de détermination de modèle de rétroaction de canal, un terminal, et un dispositif côté réseau. Le procédé comprend les étapes suivantes : réception d'un signal de référence d'informations d'état de canal envoyé par un dispositif côté réseau, et mise en œuvre d'une mesure de canal sur le signal de référence d'informations d'état de canal afin d'obtenir des informations de canal ; mise en œuvre d'un entraînement de modèle sur un modèle de rétroaction de canal cible sur la base des informations de canal, ou sélection du modèle de rétroaction de canal cible sur la base des informations de canal ; et envoi, au dispositif côté réseau, d'informations de modèle d'un modèle de décodage dans le modèle de rétroaction de canal cible.
PCT/CN2023/102375 2022-06-28 2023-06-26 Procédé de détermination de modèle de rétroaction de canal, terminal, et dispositif côté réseau WO2024002003A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021108940A1 (fr) * 2019-12-01 2021-06-10 Nokia Shanghai Bell Co., Ltd. Rétroaction d'informations d'état de canal
US20210273707A1 (en) * 2020-02-28 2021-09-02 Qualcomm Incorporated Neural network based channel state information feedback
CN113810086A (zh) * 2020-06-12 2021-12-17 华为技术有限公司 信道信息反馈方法、通信装置及存储介质
WO2022040046A1 (fr) * 2020-08-18 2022-02-24 Qualcomm Incorporated Rapport de configurations de traitement sur la base d'un réseau neuronal au niveau d'un ue
US20220060917A1 (en) * 2020-08-18 2022-02-24 Qualcomm Incorporated Online training and augmentation of neural networks for channel state feedback

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021108940A1 (fr) * 2019-12-01 2021-06-10 Nokia Shanghai Bell Co., Ltd. Rétroaction d'informations d'état de canal
US20210273707A1 (en) * 2020-02-28 2021-09-02 Qualcomm Incorporated Neural network based channel state information feedback
CN113810086A (zh) * 2020-06-12 2021-12-17 华为技术有限公司 信道信息反馈方法、通信装置及存储介质
WO2022040046A1 (fr) * 2020-08-18 2022-02-24 Qualcomm Incorporated Rapport de configurations de traitement sur la base d'un réseau neuronal au niveau d'un ue
US20220060917A1 (en) * 2020-08-18 2022-02-24 Qualcomm Incorporated Online training and augmentation of neural networks for channel state feedback

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