Measurement result sending method and apparatus, measurement result receiving method and apparatus, device and storage medium

Abstract

A measurement result sending method and apparatus, a measurement result receiving method and apparatus, a device and a storage medium, relating to the technical field of communications. The measurement result sending method is executed by a first device, the method comprising: using a measurement result of a reference signal as an input of an encoding model, wherein the encoding model outputs sequence information (510); and sending the sequence information to a second device (520). On the basis of the encoding model in the first device and a decoding model in the second device, the methods achieve a measurement and reporting mechanism based on the double-end encoding and decoding models. The first device encodes the measurement result on the basis of the encoding model and sends same to the second device, and the second device performs decoding on the basis of the decoding model to obtain the measurement result. The encoding model encodes at the sending end the measurement result and compresses the measurement result needing to be sent, and the decoding model decodes at the receiving end the received signal, so as to recover the measurement result. Thus, the overhead of the first device sending the measurement result is reduced.

Description

Method, device, apparatus and storage medium for sending measurement result TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of communication technology, in particular to a method for sending measurement result, a method for receiving measurement result, an apparatus, a device and a storage medium. BACKGROUND

[0002] In view of the great success of AI (Artificial Intelligence) technology, especially deep learning in computer vision, natural language processing and other aspects, the communication field begins to try to use deep learning to solve technical problems that traditional communication methods cannot solve, such as deep learning.

[0003] The neural network architecture commonly used in deep learning is nonlinear and data-driven, which can extract features from actual channel matrix data and restore the channel matrix information compressed by the terminal device at the base station side as much as possible. While ensuring the restoration of channel information, it also provides the possibility for the terminal device side to reduce information transmission overhead. Further discussion and research are needed on how to use deep learning technology to reduce information transmission overhead.

[0004] SUMMARY

[0005] The embodiments of the present application provide an information transmission method, apparatus, device and storage medium. The technical solutions provided by the embodiments of the present application are as follows:

[0006] According to an aspect of the embodiments of the present application, a method for sending measurement result is provided, the method is executed by a first device, and the method comprises:

[0007] Taking the measurement result of the reference signal as the input of the encoding model, outputting sequence information by the encoding model;

[0008] Sending the sequence information to a second device.

[0009] According to an aspect of the embodiments of the present application, a method for receiving measurement result is provided, the method is executed by a second device, and the method comprises:

[0010] Receiving sequence information sent by a first device;

[0011] Taking the sequence information as the input of the decoding model, outputting the measurement result of the reference signal by the decoding model.

[0012] According to an aspect of the embodiments of the present application, a sending apparatus for measurement result is provided, the apparatus comprises:

[0013] a processing module, configured to take the measurement result of the reference signal as an input of an encoding model, and output sequence information from the encoding model;

[0014] a sending module, configured to send the sequence information to the second device.

[0015] According to an aspect of some embodiments of the present application, a receiving device of a measurement result is provided, and the device comprises:

[0016] a receiving module, configured to receive sequence information sent by a first device;

[0017] a processing module, configured to take the sequence information as an input of a decoding model, and output a measurement result of a reference signal from the decoding model.

[0018] According to an aspect of some embodiments of the present application, a communication device is provided, which comprises a processor and a memory, the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned sending method or receiving method of a measurement result. The communication device is a first device, or the communication device is a second device.

[0019] According to an aspect of some embodiments of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is used to be executed by a processor to implement the above-mentioned sending method or receiving method of a measurement result.

[0020] According to an aspect of some embodiments of the present application, a chip is provided, which comprises a programmable logic circuit and / or program instructions, and when the chip is running, the programmable logic circuit and / or program instructions are used to implement the above-mentioned sending method or receiving method of a measurement result.

[0021] According to an aspect of some embodiments of the present application, a computer program product is provided, which comprises computer instructions stored in a computer readable storage medium, and a processor reads and executes the computer instructions from the computer readable storage medium to implement the above-mentioned sending method or receiving method of a measurement result.

[0022] The technical scheme provided by the embodiments of the present application can have the following beneficial effects:

[0023] Based on the encoding model of the first device and the decoding model of the second device, a measurement and reporting mechanism based on a double-end encoding and decoding model is implemented. The first device encodes the measurement result based on the encoding model and sends it to the second device, and the second device decodes the measurement result based on the decoding model. The encoding model encodes the measurement result at the sending end, compresses the measurement result to be sent, and the decoding model decodes the received signal at the receiving end, restores the measurement result, and reduces the overhead of the first device sending the measurement result. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG1 is a schematic diagram of a network architecture provided by an embodiment of the present application;

[0025] FIG2 is a schematic diagram of a fully connected neural network provided by one embodiment of the present application;

[0026] FIG3 is a schematic diagram of the structure of an AI-based CSI (Channel State Information) autoencoder provided by one embodiment of the present application;

[0027] FIG4 is a schematic diagram of beam measurement reporting in an LTM (L1 / L2 Triggered Mobility) scenario provided by one embodiment of the present application;

[0028] FIG5 is a flowchart of a method for sending and receiving measurement results provided by an embodiment of the present application;

[0029] FIG6 is a flowchart of a method for sending and receiving measurement results provided by another embodiment of the present application;

[0030] FIG7 is a flowchart of a method for sending and receiving measurement results provided by another embodiment of the present application;

[0031] FIG8 is a flowchart of a method for sending and receiving measurement results provided by another embodiment of the present application;

[0032] FIG9 is a block diagram of a device for sending measurement results according to an embodiment of the present application;

[0033] FIG10 is a block diagram of a device for receiving measurement results according to an embodiment of the present application;

[0034] FIG11 is a schematic structural diagram of a communication device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0036] The network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. A person skilled in the art will appreciate that, with the evolution of the network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0037] Referring to FIG. 1, a schematic diagram of a network architecture 100 is shown according to an embodiment of the present application. The network architecture 100 can include a terminal device 10, an access network device 20, and a core network element 30.

[0038] The terminal device 10 can refer to a UE (User Equipment), a STA (Station), an access terminal, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, a remote terminal, a mobile device, a wireless communication device, a user agent, or a user equipment. In some embodiments, the terminal device 10 can also be a cellular phone, a cordless phone, a SIP (Session Initiation Protocol) phone, a WLL (Wireless Local Loop) station, a PDA (Personal Digital Assistant), a handheld device with wireless communication function, a computing device, or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a 5GS (5th Generation System), or a terminal device in a future evolved PLMN (Public Land Mobile Network), etc., and the embodiments of the present application are not limited thereto. For the convenience of description, the above-mentioned devices are collectively referred to as terminal devices. The number of terminal devices 10 is usually multiple, and one or more terminal devices 10 can be distributed in a cell managed by each access network device 20. The terminal device can also be referred to as a terminal or a UE, and those skilled in the art can understand its meaning.

[0039] The access network device 20 is a device deployed in an access network to provide wireless communication functions for the terminal device 10. The access network device 20 can include various forms of macro base stations, micro base stations, relay stations, APs (Access Points), and the like. In systems using different wireless access technologies, the names of devices with access network device functions can be different, for example, in a 5G NR (New Radio) system, it is called gNodeB or gNB (Next Generation Node B). With the evolution of communication technology, the name of the "access network device" can change. For the convenience of description, in the embodiments of the present application, the above-mentioned devices that provide wireless communication functions for the terminal device 10 are collectively referred to as access network devices. In some embodiments, through the access network device 20, a communication relationship can be established between the terminal device 10 and the core network element 30. Illustratively, in the LTE (Long Term Evolution) system, the access network device 20 can be an EUTRAN (Evolved Universal Terrestrial Radio Access Network) or one or more eNodeBs in the EUTRAN; in the 5G NR system, the access network device 20 can be a RAN (Radio Access Network) or one or more gNBs in the RAN. In the embodiments of the present application, the "network device" refers to the access network device 20, such as a base station, unless otherwise specified.

[0040] The core network element 30 is a network element deployed in the core network, and the main functions of the core network element 30 are to provide user connection, manage users, and complete bearer for services, and provide an interface to external networks as a bearer network. For example, the core network element in the 5G NR system can include AMF (Access and Mobility Management Function) entities, UPF (User Plane Function) entities, and SMF (Session Management Function) entities.

[0041] In some embodiments, the access network device 20 and the core network element 30 communicate with each other through some air interface technology, such as the NG interface in the 5G NR system. The access network device 20 and the terminal device 10 communicate with each other through some air interface technology, such as the Uu interface.

[0042] The "5G NR system" in the embodiments of the present application can also be referred to as a 5G system or an NR system, but those skilled in the art can understand its meaning. The technical solutions described in the embodiments of the present application can be applicable to the LTE system, and can also be applicable to the 5G NR system, and can also be applicable to the subsequent evolution system (for example, the B5G (Beyound 5G) system, the 6G system (6th Generation System, the sixth generation mobile communication system)) of the 5G NR system, and can also be applicable to other communication systems such as the NB-IoT (Narrow Band Internet of Things, Narrow Band Internet of Things) system, and the present application does not limit this.

[0043] In the embodiments of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resource (for example, the frequency domain resource, or the spectrum resource) on the carrier used by the cell. The cell can be a cell corresponding to the network device (for example, a base station), and the cell can belong to a macro base station or a base station corresponding to a small cell (Small cell). The small cell here can include a metro cell, a micro cell, a pico cell, a femto cell, etc. These small cells have the characteristics of small coverage and low transmit power, and are suitable for providing high-speed data transmission services.

[0044] Before introducing the technical solutions of the present application, some related technical knowledge involved in the present application will be introduced and explained. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and all belong to the protection scope of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0045] I. Measurement and reporting of NR beam management

[0046] In related technologies, the beam management mechanism is standardized.

[0047] Beam management mainly includes beam related measurement and reporting, and beam indication by NW (Network) according to UE's reporting. Beam measurement is based on periodic, semi-persistent and aperiodic reference signals. The reference signals are CSI-RS (CSI-Reference Signal) or SSB (Synchronization Signal and PBCH Block). UE measures one or more SSB resource sets and / or CSI-RS resource sets. The best N (1, 2 or 4) reference signal resource indexes (i.e. SSB RI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator)) and corresponding link quality (with L1-RSRP (L1-Reference Signal Received Power) or L1-SINR (L1-Signal to Inference and Noise Ratio) as indicators) are reported to NW.

[0048] The specific reporting format is shown in Table 1, where L1-RSRP is reported in a differential manner.

[0049] Beam reporting is associated with measurement resources, which include periodic beam reporting, such as using PUCCH (Physical Uplink Control Channel) resources for reporting; beam reporting can also be semi-persistent reporting, such as using PUCCH or PUSCH resources for reporting, and semi-persistent reporting can use MAC CE (Media Access Control-Control Element) to activate or deactivate; beam reporting can also be aperiodic reporting, such as using PUSCH (Physical Uplink Shared Channel) resources, and aperiodic measurement reporting is triggered based on NW sending DCI (Downlink Control Information).

[0050] Table 1: UCI (Uplink Control Information) format for traditional beam measurement reporting

[0051] When the beam measurement report is encoded, the UE inserts CRC (Cyclic Redundancy Check) check bits to facilitate the NW to determine whether the decoding of the report is correct.

[0052] II. Measurement and reporting of LTM

[0053] In the related art, the NR system only supports layer 3 (i.e., RRC (Radio Resource Control) layer) mobility. The NR standardizes the enhancement of layer 1 / layer 2 triggered mobility (LTM). From the perspective of the physical layer (i.e., L1), the UE pre-measures multiple candidate cells (including the current serving cell) through the downlink measurement resource configured by the NW, and then reports the measurement results to the NW. The NW indicates the selected candidate cell (i.e., target cell) and / or indicates the candidate beam through the cell switching command.

[0054] For measurement and reporting, the UE can measure in multiple candidate cells (including the current serving cell) configured by the NW, and give at most M x L (depending on the UE capability, L is the number of candidate cells, and M is the number of beams per cell) measurement results in one report. Obviously, for the measurement of a large number of candidate cells, a large amount of reference signal overhead is required, and the time delay required for the UE to measure these reference signals, after all, the UE cannot measure multiple beams of multiple candidate cells at the same time.

[0055] When the UE completes the measurement and reports the measurement results to the NW, the NW can indicate the UE to switch to the target cell through the MAC CE of the CSC (Cell Switch Command), which at least includes the target cell information (such as candidate cell index) and / or the selected beam information (i.e., indicated by a unified TCI state ID (s)).

[0056] III. Measurement and reporting of data collection

[0057] In the AI / ML-based beam management in the related art, data collection for training the model often requires the UE to perform a large amount of measurement and reporting (assuming the model on the NW side).

[0058] For example, for the training of the model, the measurement results of Set B are needed as the input of the model, and Set A is needed as the label of the output of the model. The input of the model and the corresponding label can be considered as a sample of data. The training of the model requires a large number of such samples.

[0059] For BM-Case1 (use case of spatial-domain beam prediction), Set B is a smaller set of measurements relative to Set A, such as a set of reference signals corresponding to only 8 measurement beams. Set A can be considered as a full set of beams, such as a set of 64 predicted beams.

[0060] Four, AL / ML (Maching Learning, machine learning) model introduction

[0061] Deep neural network

[0062] A simple neural network is shown in FIG. 2, which includes an input layer, a hidden layer, and an output layer. Through different connection modes of multiple neurons, weights, and activation functions, different outputs can be generated, and then the mapping relationship from input to output can be fitted. Each upper node is connected to all lower nodes. This full connection model can also be called a DNN (Deep Neural Networks, deep neural network) in the present case, i.e., a deep neural network. The DNN model can perform spatial-domain beam prediction.

[0063] Five, beam management use case based on AL / ML

[0064] In the ongoing 3GPP R18 discussion, AI / ML-based beam management is one of the main use cases of R18 AI projects. RAN1 is preparing to standardize spatial and / or temporal beam prediction mechanisms in R19. Spatial-domain (BM-Case1) beam prediction and time-domain (BM-Case2) beam prediction are defined as follows.

[0065] Agreement protocol

[0066] For AI / ML-based beam management, support BM-Case1 and BM-Case2 for characterization and baseline performance evaluations

[0067] BM-Case1: Spatial-domain DL beam prediction for Set A of beams based on measurement results of Set B of beams

[0068] • BM-Case2: Temporal DL beam prediction for Set A of beams based on the historic measurement results of Set B of beams

[0069] • FFS: details of BM-Case1 and BM-Case2

[0070] • FFS: other sub use cases

[0071] Note: For BM-Case1 and BM-Case2, Beams in Set A and Set B can be in the same Frequency Range

[0072] Where Set B is the measurement set of UE as the input of the model; Set A is the prediction set of the model, which is related to the output of the model. For some BM-Case1 / BM-Case2 model training, it is necessary to collect the measurement results of Set B (as the input of the model) and the measurement results of Set A (as the label of the model).

[0073] Six, SL / ML-based CSI feedback method

[0074] In view of the great success of AI technology, especially deep learning in computer vision, natural language processing and other aspects, the communication field began to try to use deep learning to solve the technical problems that traditional communication methods are difficult to solve, such as deep learning. The neural network architecture commonly used in deep learning is nonlinear and data-driven, which can extract features from actual channel matrix data and restore the channel matrix information compressed by the UE side at the base station side as much as possible, which not only guarantees the restoration of channel information, but also provides the possibility for the UE side to reduce the CSI feedback overhead.

[0075] For example, the deep learning-based CSI feedback regards the channel information as a "image" to be compressed, uses a deep learning auto-encoding model to compress the input channel information, and reconstructs the compressed channel "image" at the receiving end, which can retain the channel information to a greater extent.

[0076] The AI-based CSI self-encoding model method is divided into an encoding model and a decoding model, which are respectively deployed in the UE (as the sending end of the CSI feedback) and the NW (as the receiving end of the CSI feedback). Therefore, it can also be called a double-end model based on AI / ML.

[0077] Specifically, the UE obtains channel information through channel estimation, which is input to the encoding model. The channel information matrix is compressed and encoded through the neural network of the encoding model, and the compressed bit sequence is fed back to the NW through the air interface feedback link. Correspondingly, the NW recovers the channel information according to the feedback bit sequence through the decoding model, and outputs the complete feedback channel information.

[0078] The AI / ML model of the encoding model and the decoding model shown in FIG. 3 can use a DNN composed of multiple fully connected layers, or a CNN (Convolutional Neural Networks) composed of multiple convolutional layers, or an RNN with structures such as LSTM (Long Short-Term Memory) and GRU (Gate Recurrent Unit), and various neural network architectures such as residual and self-attention mechanisms can also be used to improve the performance of the encoding model and the decoding model.

[0079] The above-mentioned CSI input and output can be full channel information or feature vector information obtained based on full channel information. The feedback method based on the feature vector is the feedback architecture supported in the current NR system. It can achieve higher CSI feedback accuracy with the same feedback bit overhead, or significantly reduce the feedback overhead while achieving the same CSI feedback accuracy.

[0080] For traditional beam-related measurement reporting, the UE needs to insert CRC check bits after completing source encoding. The NW needs to verify whether the UCI is decoded correctly by CRC check after decoding the report. For a small amount of reporting, the CRC check bit ratio is high, so it will bring a certain feedback overhead.

[0081] The CSI feedback of the channel (mainly the precoding matrix) itself has a certain fault tolerance, that is, the receiving end does not need to recover 100% of the information (compressed information) of the sending end, but only needs to generally recover the CSI to achieve acceptable performance in precoding. However, the current research mainly focuses on the feedback of the CSI, and neither the double-end source encoding scheme, the joint source channel encoding scheme, nor the joint source channel encoding and modulation scheme considers the beam-related measurement reporting. This is also the focus of the present case.

[0082] In FIG. 4, taking the measurement and reporting of LTM as an example. The UE needs to measure multiple candidate beams of multiple candidate cells, and select the M*L measurement contents with the highest link quality to report. An analogy can be made with an image, and each candidate cell is a region of the entire image. In each region, the measurement result of each beam can be regarded as a "pixel" of the image.

[0083] When the measurement pixels of the UE increase, the correlation (or redundancy) between multiple pixels gradually increases. This provides sufficient space for compressing the measurement reporting.

[0084] Referring to FIG. 5, a flowchart of a sending method and a receiving method of measurement results is shown. The method is interactively executed by a first device and a second device, and the method includes at least one of the following steps 510-530.

[0085] Step 510, the first device takes the measurement result of the reference signal as the input of the encoding model, and outputs the sequence information from the encoding model.

[0086] In some embodiments, the reference signal is a known signal provided by the transmitting end to the receiving end for channel estimation or channel sounding. Illustratively, the uplink reference signal is used for uplink channel estimation or uplink channel quality measurement. For example, the uplink reference signals DM-RS (Demodulation Reference Signal) and SRS (Sounding Reference Signal). The DM-RS is associated with the transmission of PUSCH and PUCCH, and is used to calculate the channel estimation matrix to help the demodulation of the two channels. The SRS is independently transmitted, and is used for the estimation of uplink channel quality and channel selection, and the calculation of the SINR of the uplink channel. Illustratively, the downlink reference signal is used for downlink channel estimation, downlink channel quality measurement or cell search. For example, the downlink reference signals CRS, PRS and CSI-RS. The CRS (Cell Reference Signal) (cell-specific reference signal, also called common reference signal) is used for channel estimation and related demodulation of all downlink transmission technologies except the beamforming technology based on the codebook. Cell-specific means that the reference signal corresponds to the antenna port (antenna port 0-3) of a network device end. The PRS (Positioning Reference Signal) is a positioning reference signal, which is used to assist the receiving end in positioning. The CSI-RS is a channel state information reference signal, which is used to obtain channel state information.

[0087] In some embodiments, the encoding model is an AI or ML model. An AI model refers to a mathematical representation obtained by running a deep learning algorithm based on an existing data set. It is a mathematical model that converts data into intelligence. AI models can be used to analyze, process, predict, and optimize data with certain regularity and predictability. AI models need to be professionally trained and trained before being applied in different applications. Through continuous data feeding to the machine and optimization of framework algorithms, the corresponding computing power is matched to complete the task. AI models have the characteristics of being powerful, efficient, and flexible. A ML model is an intelligent file that predefines an algorithm, can learn a specific pattern of data set, and extract insights from it to make predictions. When creating a ML model, the answer to be obtained needs to be defined, and the working and learning parameters of the model need to be set. When the ML model starts processing new data, actionable insights can be obtained. The training data must contain the correct answers, which are called targets or target attributes. The learning algorithm finds patterns in the training data that map input data attributes to targets, and then outputs a ML model that captures these patterns. AI models are classified based on learning methods, which can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0088] In some embodiments, the reference signal is used for measurement related to the spatial transmission filter.

[0089] Spatial filtering refers to a technique that uses the spatial separation of signals from different directions to achieve spatial processing of signals when several signals stacked together in time domain occupy the same frequency band. The concept of spatial filtering is proposed in the field of digital beam forming technology (DBF) in radar technology. It refers to forming a main lobe beam in a specific direction to receive useful signals and suppress interference signals from other directions. Because several signals stacked together or arriving at the same time in time domain occupy the same frequency band, conventional time domain filtering and frequency domain filtering cannot separate them, but these signals are generally from different directions. Beam forming is to use the spatial separation to achieve spatial processing of signals. In essence, it is a multi-channel array signal processing system, which is a concept of spatial filtering. In the embodiments of the present application, the spatial transmission filter can also be referred to as a beam.

[0090] In some embodiments, the first device measures N measurement resources to obtain measurement result data corresponding to the N measurement resources respectively, and N is a positive integer.

[0091] In some embodiments, the measurement result includes all or part of the measurement result data of at least one measurement resource. In some embodiments, the measurement result can include the measurement result data of all the above N measurement resources, or only include the measurement result data of part of the above N measurement resources, which is not limited in the present application.

[0092] In some embodiments, the link corresponding to the measurement resource is a beam. In some embodiments, the links corresponding to different measurement resources are different beams. For example, the link corresponding to measurement resource 1 is beam 1, the link corresponding to measurement resource 2 is beam 2, and the link corresponding to measurement resource 3 is beam 3. In some embodiments, the links corresponding to different measurement resources can be beams in the same cell or beams in different cells, which are not limited in the present application. For example, beam 1 and beam 2 are beams in cell 1, and beam 3 is a beam in cell 2.

[0093] In some embodiments, the measurement result data of each measurement resource includes: an index of the measurement resource, and / or a link quality corresponding to the measurement resource.

[0094] In some embodiments, the link quality corresponding to the measurement resource can be at least one of: L1-RSRP, L1-RSRQ (L1-Reference Signal Received Quality), and L1-SINR.

[0095] In some embodiments, the index of the measurement resource can be an index of the link corresponding to the measurement resource, such as SSB RI, or an index of the reference signal transmitted on the measurement resource, such as CRI, which is not limited in the present application.

[0096] In some embodiments, the measurement result data of all or part of the at least one measurement resource includes:

[0097] measurement result data of an optimal reference signal in a cell; or

[0098] measurement result data of an optimal reference signal in L candidate cells, L being a positive integer; or

[0099] measurement result data of a plurality of reference signals.

[0100] In some embodiments, in different application scenarios, the content included in the measurement result data of all or part of the at least one measurement resource is different. For example, in a beam management measurement reporting scenario, the measurement result data of all or part of the at least one measurement resource includes measurement result data of an optimal reference signal in a cell; in an LTM measurement reporting scenario, the measurement result data of all or part of the at least one measurement resource includes measurement result data of an optimal reference signal in L candidate cells; and in a data collection measurement reporting scenario, the measurement result data of all or part of the at least one measurement resource includes measurement result data of a plurality of reference signals.

[0101] In some embodiments, the number of optimal reference signals in a cell is not limited in the present application. For example, the number of optimal reference signals is M, where M is a positive integer. For example, M can be 1 or 2 or 4.

[0102] In some embodiments, the number of optimal reference signals in a cell is indicated by the network device. In some embodiments, the number of optimal reference signals in a cell is indicated by RRC signaling. In some embodiments, the number of optimal reference signals in different cells can be the same or different, which is not limited in the present application. In some embodiments, if the measurement result data of all or part of the at least one measurement resource includes the measurement result data of the optimal reference signals in L candidate cells, where L is greater than 1, the RRC signaling can indicate the number of optimal reference signals corresponding to the L candidate cells respectively. For example, the number of optimal reference signals corresponding to candidate cell 1 is 2, and the number of optimal reference signals corresponding to candidate cell 2 is 4. In some embodiments, if the measurement result data of all or part of the at least one measurement resource includes the measurement result data of the optimal reference signals in L candidate cells, where L is greater than 1, the number of optimal reference signals corresponding to the L candidate cells is the same. For example, the number of optimal reference signals corresponding to candidate cell 1 and candidate cell 2 is 2.

[0103] In some embodiments, the number of multiple reference signals can also be indicated by the network device, for example, by RRC signaling. In some embodiments, the number of multiple reference signals can also be determined by the first device itself, or be predefined or preconfigured, which is not limited in the present application. For example, it can be determined by the first device based on the capability of the encoding model.

[0104] In some embodiments, the sequence information does not have corresponding check bits. In some embodiments, the sequence information does not have CRC or the like. In some embodiments, even if the decoding model cannot perfectly recover the input of the encoding model, the result obtained by the decoding model can still be used, and there is no great impact on the link performance. Specifically, for the index of the measurement resource, when there are a large number of and subdivided beams in the system, the network device can plan such that adjacent beams have similar spatial directivity, and adjacent beams correspond to adjacent resource indexes. Therefore, even if the resource index recovery is wrong, it is possible that there is no very large fluctuation of link quality. For example, assuming that the system uses 64 downlink transmission beams, corresponding to resource indexes 0 to 63, when the index of the measurement resource input by the encoding model is 24, the output of the decoding model can be the index of the measurement resource 23, which is not correct, but the coverage of the downlink beam does not have a large gap. Similarly, for the recovery of the link quality corresponding to the measurement resource, for example, the L1-RSRP of the measurement resource index 24 input into the encoding model is -64 dBm, but the value recovered by the decoding model is -60 dBm, which is within an acceptable error range. The first device transmits the signal to the second device, and the signal needs to carry less content due to the reduction of the check bits, thereby reducing the overhead of the first device in signal transmission.

[0105] In some embodiments, the sequence information can only be decoded by the decoding model on the second device side.

[0106] Step 520: The first device transmits the sequence information to the second device.

[0107] Correspondingly, the second device receives the sequence information transmitted by the first device.

[0108] Step 530: The second device takes the sequence information as the input of the decoding model, and outputs the measurement result of the reference signal by the decoding model.

[0109] In some embodiments, the decoding model is an AI or ML model. In some embodiments, the decoding model and the encoding model can have the same model structure, or can not have the same model structure, which is not limited in the present application. For example, the encoding model adopts a full connection model structure, and the decoding model adopts a CNN model structure. For example, the encoding model and the full connection model both adopt a full connection model structure, but the number of hidden layers in the two is different.

[0110] In some embodiments, the first device can be a terminal device or a network device. In some embodiments, the second device can be a terminal device or a network device. In some embodiments, the communication between the first device and the second device can be uplink communication, downlink communication, or sidelink communication, which is not limited in the present application. In some embodiments, if the first device is a network device and the second device is a terminal device, the reference signal in step 510 can be implemented as an uplink reference signal. In some embodiments, if the first device and the second device are both terminal devices, the reference signal in step 510 can be implemented as a sidelink reference signal. In some embodiments, if the first device is a terminal device and the second device is a network device, the reference signal in step 510 can be implemented as a downlink reference signal.

[0111] The technical scheme provided by the embodiments of the present application realizes a measurement and reporting mechanism based on a double-end encoding and decoding model based on the encoding model of the first device and the decoding model of the second device. The first device encodes the measurement result based on the encoding model and sends it to the second device, and the second device decodes the measurement result based on the decoding model. The encoding model encodes the measurement result at the sending end, compresses the measurement result to be sent, and the decoding model decodes the received signal at the receiving end to restore the measurement result, thereby reducing the overhead of the first device sending the measurement result.

[0112] In some embodiments, the encoding model has several encoding methods for the measurement result, and different encoding methods will bring different effects, which are classified and described in the following embodiments.

[0113] Method one, the encoding model is used for source encoding of the measurement result

[0114] In some embodiments, after the encoding model source encodes the measurement result, the output sequence information is a bit sequence.

[0115] In some embodiments, the above step 520 can be implemented as the following step 521.

[0116] Step 521, the first device sends the sequence information to the second device after channel encoding, modulation, resource mapping and antenna mapping.

[0117] Correspondingly, the second device obtains the sequence information after radio frequency receiving, resource demapping, demodulation and channel decoding of the signal from the first device.

[0118] Exemplarily, refer to FIG. 6, which shows a flowchart of a measurement result sending method and a receiving method provided by an embodiment of the present application.

[0119] After the first device performs the relevant measurement, the measurement result data is taken as the input of the encoding model, and the encoding model outputs sequence information. The sequence information is then subjected to conventional channel encoding (such as LDPC (Low-density Parity-check), Polar, Block Code, etc.), modulation, resource mapping, antenna mapping, and other processes, and is then transmitted to the second device. Correspondingly, the second device obtains the sequence information after performing radio frequency reception, resource demapping, demodulation, and channel decoding on the received signal.

[0120] In some embodiments, the above method can be applied in different scenarios, and the following are examples of several possible application scenarios.

[0121] Example 1, beam management measurement reporting

[0122] Taking a terminal device (such as a UE) as the first device and a network device (such as a NW) as the second device as an example, according to the conventional beam reporting in NR, the input of the encoding model on the UE side can be the measurement result data of the optimal reference signal in the measurement resource. The number of optimal spatial domain propagation filters is determined according to RRC configuration and can be 1, 2, or 4. The input of the encoding model can be the index of the measurement resource and the link quality corresponding to the measurement resource.

[0123] The output of the decoding model on the NW side is the index of the optimal N measurement resources and the link quality corresponding to the N measurement resources, respectively.

[0124] The technical advantage of using the double-end model of the encoding model and the decoding model is that when the decoding model needs to recover the index of the measurement resource corresponding to the optimal spatial domain transmission filter, the four beams often have similar spatial directivity and close link quality, such as L1-RSRP. Therefore, the reporting content of the source encoding and decoding has a certain compression space, which can reduce the reporting overhead.

[0125] In addition, when the index and / or link quality of one of the measurement resources is recovered incorrectly, the NW can still refer to the resource index and link quality corresponding to the other three correctly recovered beams. Therefore, the reporting content of the source encoding and decoding has a certain fault tolerance.

[0126] Example 2, measurement reporting of LTM

[0127] Taking the first device as a terminal device (such as a UE) and the second device as a network device (such as a NW) as an example, for the scenario of LTM measurement reporting, compared with the measurement of one cell in beam management, the measurement and reporting range of LTM is expanded from one cell to multiple candidate cells (including the current serving cell). In LTM, the UE needs to report the indexes of the measurement resources corresponding to the M optimal spatial transmission filters of the L candidate cells and the link quality, where M = 1, 2 or 4; L is the number of candidate cells.

[0128] Similarly, the input of the encoding model can be all or part of the measurement results of the L candidate cells. The output of the decoding model is the index of the measurement resource corresponding to the M optimal spatial transmission filters of the L cells and / or the link quality.

[0129] More obviously, the technical advantage of the two-end model is that when the decoding model needs to recover the indexes of the measurement resources corresponding to the multiple optimal spatial transmission filters of multiple cells and / or the link quality, the beam spatial pointing of the adjacent cells is likely to have a certain correlation (for a certain UE), and the link quality is closer. Therefore, there is a compression space for the reporting content with more. Using the two-end model can further reduce the reporting overhead.

[0130] In addition, when the index of the measurement resource and / or the link quality of a certain candidate cell is recovered incorrectly, the NW can still refer to the indexes of the measurement resources corresponding to the spatial transmission filters and the link quality recovered correctly by other candidate cells. Therefore, it has a certain fault tolerance.

[0131] Example 3, measurement reporting of data collection

[0132] For the AI / ML-based beam prediction mechanism, in order to train the model (including the encoding model and the decoding model), the UE needs to collect a large amount of data. The training of the model is often deployed on the NW side, because the NW side is easier to collect a large amount of data (multiple UEs), has more computing resources (such as GPU), and is less sensitive to power consumption. Therefore, in this case, the UE needs to report a large amount of measurement samples to the NW through physical layer or high layer signaling.

[0133] The data set contains a large number of samples. Each sample contains the input part and the label part of the model. For example, for the input part, the UE needs to report the measurement results of a set containing a small amount of measurement resources (such as Set B); for the label part, the UE may need to report a set containing a large amount of measurement resources (such as Set A). When Set B is a subset of Set A, the UE only needs to report Set A.

[0134] The two-end AI / ML model is used to train a single-end AI / ML model for data collection. This solution can be understood as an "AI for AI" solution.

[0135] The input of the encoding model can be the measurement results of Set B and / or Set A. As mentioned above, the measurement results contain the indices of the measurement resources and / or the corresponding link quality. In the case of fixed resources of Set B and Set A, the indices of the measurement resources within the set can be encoded in an implicit manner, reflected in the ordering of the link quality, in order to save the reporting overhead. In other embodiments, if Set B and / or Set A are variable, the encoding model should explicitly input the indices of the measurement resources and the link quality corresponding to Set B and / or Set A during data collection.

[0136] The output of the decoding model is the input and label required for model training, i.e., the indices of the measurement resources and / or the link quality of Set B and / or Set A. For Set A, it covers the information of the entire set of beams of a cell. Considering that two adjacent beams have similar spatial directivity and the link quality is also generally comparable, the encoding model has a large compression space for Set A. This is also a technical advantage of the solution. In addition, for training data, it is often not required to be completely correct, and the model training can be trained well and has certain generalization performance. Not to mention that there may be data drift due to measurement errors in the process of beam measurement. Similarly, the encoding and decoding solution of the two-end model itself also has certain fault-tolerant features.

[0137] Method two, the encoding model is used for joint source-channel encoding of the measurement results

[0138] In some embodiments, after the encoding model performs joint source-channel encoding on the measurement results, the output sequence information is a bit sequence.

[0139] In some embodiments, the above step 520 can be implemented as the following step 522.

[0140] Step 522, after the first device modulates, resource maps and antenna maps the sequence information, the first device sends the sequence information to the second device.

[0141] Correspondingly, after the second device performs radio frequency reception, resource demapping and demodulation on the signal from the first device, the second device obtains the sequence information.

[0142] Exemplarily, refer to FIG. 7, which shows a flowchart of a measurement result sending method and a measurement result receiving method provided by an embodiment of the present application.

[0143] After the first device performs the relevant measurement, the measurement result data is taken as the input of the encoding model, and the encoding model outputs sequence information. After the sequence information is modulated, resource mapped, and antenna mapped, etc., it is sent to the second device. Correspondingly, the second device obtains the sequence information after performing radio frequency reception, resource demapping, and demodulation on the received signal.

[0144] Compared with the first mode, the first mode uses an encoding model and a decoding model to replace the source encoding and decoding, and the purpose is to reduce the redundancy of source encoding, i.e., to reduce the overhead. On the contrary, the second mode uses an encoding model and a decoding model to replace the source encoding and decoding and the channel encoding. The purpose of replacing the channel encoding is to increase the redundancy to resist the wireless fading channel and / or the noise channel. The joint source-channel encoding is a reasonable compromise between the two. Compared with the first mode, the technical advantage of the second mode is that the channel encoding is fused, and the statistical characteristics of the source data are used for channel encoding. This cannot be achieved by the traditional channel encoding (without source statistical information). Thus, under the condition of a certain number of encoding bits, better error protection can be obtained, and the decoding model can more realistically recover the encoding information. The output of the encoding model has an implicit channel encoding function and has a certain error protection function, which can resist the fading of the wireless channel and the noise. In addition, considering that the beam-related measurement result has a certain fault tolerance, similarly, CRC check bits can not be added after the output of the joint source-channel encoding model, so as to reduce the overhead.

[0145] In some embodiments, the above method can be applied in different scenarios, and the following are examples of several possible application scenarios.

[0146] Example 1, beam management measurement reporting

[0147] Taking the first device as a terminal device (such as UE) and the second device as a network device (such as NW) as an example, the input of the UE-side encoding model can be the measurement result data of the optimal reference signal in the measurement resource. The number of optimal spatial domain propagation filters is determined according to RRC configuration, which can be 1, 2, or 4. The input of the encoding model can be the index of the measurement resource and the link quality corresponding to the measurement resource. The output of the UE-side encoding model is also a sequence of information, but the channel encoding has been implicitly performed and does not need to insert CRC check code.

[0148] As the input of the NW-side decoding model, it is the sequence information that has not been channel decoded. The decoding model jointly performs channel decoding and source decoding operations, thereby recovering the reporting content, i.e., the index of the measurement resource corresponding to the optimal spatial domain transmission filter and / or the link quality.

[0149] In addition, from the perspective of the compression of the reported content, in addition to the spatial directivity between beams can be compressed, different wireless channel environments can use channel coding with different degrees of redundancy for protection. For example, when the SNR (Signal-to-Noise Ratio) is high, fewer redundant bits can be used for implicit channel coding; conversely, when the SNR is low, more redundant bits can be used for implicit channel coding. Thus, there can be a compromise between the source and channel coding that adapts to the environment.

[0150] Finally, this approach also retains the fault tolerance of beam reporting information, that is, when part of the reported information is recovered incorrectly, the NW deploying the decoding model can still refer to other correctly recovered beam information to perform subsequent work such as beam indication.

[0151] Example 2, Measurement reporting of LTM

[0152] Similarly, the input of the encoding model can be all or part of the measurement results of the L candidate cells. The encoding model jointly encodes the measurement results. And no CRC check code is inserted.

[0153] The input of the decoding model is the information with implicit channel coding and source coding. The output of the decoding model is the index of the measurement resource corresponding to the optimal M spatial transmission filters of the L cells and / or the link quality.

[0154] Joint source-channel coding can also balance the error resistance performance and the recovery fidelity according to the coding of the wireless channel environment. Similarly, this approach also has compressibility and fault tolerance for the reported content.

[0155] Example 3, Measurement reporting of data collection

[0156] Compared with Example 3 in the above embodiment mode one, the input of the encoding model in this embodiment is also the measurement results of Set B and / or Set A. But the output of the encoding model implies part of the channel coding and the source coding.

[0157] Similarly, the input of the decoding model also contains the sequence information jointly encoded by the joint source channel. The output of the decoding model is the input and label required for model training, i.e., the index of the measurement resource and / or the link quality of Set B and / or Set A. For Set A, it covers the information of the entire set of beams of a cell. Considering that two adjacent beams have similar spatial directivity and the link quality is also generally comparable, the encoding model can have a large compression space for Set A. This is also a technical advantage of the present approach. In addition, for training data, it is often not required to be completely correct, and the model training can be trained well and has a certain generalization performance. Not to mention that there may be data drift due to measurement errors in the process of beam measurement. Similarly, the encoding and decoding of the two-end model itself also has certain fault tolerance features. The joint source channel encoding can also balance the error resistance performance and the fidelity of recovery according to the encoding of the wireless channel environment. The present approach is stronger in compressibility and fault tolerance of the reported content.

[0158] Method three, the encoding model is used for joint source channel encoding and modulation of the measurement result

[0159] In some embodiments, after the encoding model performs joint source channel encoding and modulation on the measurement result, the output sequence information is a complex sequence.

[0160] In some embodiments, the above step 520 can be implemented as the following step 523.

[0161] Step 523, the first device sends the sequence information to the second device after resource mapping and antenna mapping.

[0162] Correspondingly, the second device obtains the sequence information after radio frequency receiving and resource demapping of the signal from the first device.

[0163] Exemplarily, please refer to FIG. 8, which shows the flowchart of the measurement result sending method and receiving method provided by an embodiment of the present application.

[0164] After the first device performs the related measurement, the measurement result data is taken as the input of the encoding model, and the encoding model outputs the sequence information. Then, after the sequence information is subjected to resource mapping, antenna mapping and other processes, it is sent to the second device. Correspondingly, the second device obtains the sequence information after radio frequency receiving and resource demapping of the received signal.

[0165] Compared with the third embodiment, the encoding model and the decoding model in this embodiment perform modulation and demodulation in addition to source channel encoding and decoding of the measurement results. The technical advantage of this embodiment is that modulation is performed after source channel encoding, which can modulate the encoded content to different constellation points, thereby allocating different powers to different information bits to improve the signal-to-noise ratio of important information bits. Considering the power control of the uplink, the constellation points used in this mode need to ensure that the total power is limited on one OFDM transmission symbol. In addition, the output of the encoding model is not sequence information, which does not bring quantization error. The last advantage is that each constellation point output by the encoding model can contain the input information, so it is more robust than the traditional scheme. The input of the encoding model is the same as that of the second embodiment. However, the output of the encoding model is no longer sequence information, but a series of complex numbers used to map constellation points on the complex plane. The input of the decoding model is also a complex number because the decoding model needs to perform demodulation, channel decoding and source decoding.

[0166] In some embodiments, the above method can be applied in different scenarios, and the following are examples of several possible application scenarios.

[0167] Example 1, beam management measurement reporting

[0168] Taking a first device as a terminal device (such as a UE) and a second device as a network device (such as a NW) as an example, the input of the UE-side encoding model can be the measurement result data of the optimal reference signal in the measurement resource. The number of optimal spatial propagation filters is determined according to RRC configuration and can be 1, 2 or 4. The input of the encoding model can be the index of the measurement resource and the link quality corresponding to the measurement resource. The output of the UE-side encoding model is a series of complex numbers used to map constellation points on the complex plane.

[0169] The input of the NW-side decoding model is a complex number that has not been demodulated. The decoding model jointly performs demodulation, channel decoding and source decoding operations to recover the reported content, that is, the index and / or link quality of the measurement resource corresponding to the optimal spatial transmission filter.

[0170] Example 2, measurement reporting of LTM

[0171] Similarly, the input of the encoding model can be all or part of the measurement results of the L candidate cells. The encoding model jointly performs source channel modulation and encoding on the measurement results.

[0172] The input of the decoding model is information with implicit modulation, channel encoding and source encoding. The output of the decoding model is the index and / or link quality of the measurement resource corresponding to the optimal M spatial transmission filters of the L cells.

[0173] Example 3, measurement report of data collection

[0174] Compared with Example 3 in the above embodiment mode one, the input of the encoding model in this embodiment is also the measurement result of Set B and / or Set A. But the output of the encoding model implies part of modulation, channel coding and source coding.

[0175] Similarly, the input of the decoding model also contains the complex number of joint modulation, source coding and channel coding. The output of the decoding model is the input and label required for model training, that is, the index of the measurement resource of Set B and / or Set A and / or the link quality.

[0176] In the above method embodiment, the technical solutions of the present application are introduced and explained only from the perspective of the interaction between the first device and the second device. The steps performed by the first device described above can be implemented alone to become a sending method of the measurement result of the first device side, and the steps performed by the second device described above can be implemented alone to become a receiving method of the measurement result of the second device side. In addition, the embodiments provided in the present application can be combined arbitrarily to form new embodiments, which are all within the protection scope of the present application.

[0177] The following is a device embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0178] Please refer to FIG. 9, which shows a block diagram of a measurement result sending device provided by an embodiment of the present application. The device has the functions of implementing the above-mentioned measurement result sending method examples, which can be implemented by hardware or by executing corresponding software by hardware. The device can be the first device introduced above or can be arranged in the first device. As shown in FIG. 9, the device 900 can include a processing module 910 and a sending module 920.

[0179] The processing module 910 is configured to take the measurement result of the reference signal as the input of the encoding model, and output sequence information by the encoding model.

[0180] The sending module 920 is configured to send the sequence information to the second device.

[0181] In some embodiments, the encoding model is configured to source encode the measurement result.

[0182] In some embodiments, the sending module 920 is configured to send the sequence information to the second device after channel coding, modulation, resource mapping and antenna mapping.

[0183] In some embodiments, the encoding model is configured to jointly source and channel encode the measurement result.

[0184] In some embodiments, the sending module 920 is configured to send the sequence information to the second device after modulation, resource mapping and antenna mapping of the sequence information.

[0185] In some embodiments, the encoding model is configured to jointly source-channel encode and modulate the measurement result.

[0186] In some embodiments, the sending module 920 is configured to send the sequence information to the second device after resource mapping and antenna mapping of the sequence information.

[0187] In some embodiments, the measurement result comprises all or part of measurement result data of at least one measurement resource.

[0188] In some embodiments, the measurement result data of each measurement resource comprises an index of the measurement resource and / or a link quality corresponding to the measurement resource.

[0189] In some embodiments, the all or part of measurement result data of the at least one measurement resource comprises:

[0190] measurement result data of an optimal reference signal in one cell; or

[0191] measurement result data of an optimal reference signal in L candidate cells, L being a positive integer; or

[0192] measurement result data of a plurality of reference signals.

[0193] In some embodiments, the sequence information does not have corresponding check bits.

[0194] In some embodiments, the encoding model is an AI or ML model.

[0195] In some embodiments, the reference signal is used for measurement related to a spatial domain transmission filter.

[0196] The technical scheme provided by the embodiments of the present application realizes a measurement and reporting mechanism based on a double-end encoding and decoding model based on an encoding model of a first device and a decoding model of a second device. The first device encodes a measurement result based on the encoding model and sends it to the second device, and the second device obtains the measurement result after decoding based on the decoding model. The encoding model encodes the measurement result at the sending end, compresses the measurement result to be sent, and the decoding model decodes the received signal at the receiving end to restore the measurement result, thereby reducing the overhead of the first device sending the measurement result.

[0197] Please refer to FIG. 10, which shows a block diagram of a device for receiving measurement results according to an embodiment of the present application. The device has the functions of the above-mentioned example of a method for receiving measurement results, which can be implemented by hardware, or by executing corresponding software by hardware. The device can be the second device described above, or can be arranged in the second device. As shown in FIG. 10, the device 1000 can include a receiving module 1010 and a processing module 1020.

[0198] The receiving module 1010 is configured to receive sequence information sent by a first device.

[0199] The processing module 1020 is configured to input the sequence information into a decoding model, and output measurement results of reference signals from the decoding model.

[0200] In some embodiments, the decoding model is configured to source decode the sequence information to obtain the measurement results.

[0201] In some embodiments, the receiving module 1010 is configured to obtain the sequence information after performing radio frequency receiving, resource demapping, demodulation and channel decoding on a signal from the first device.

[0202] In some embodiments, the decoding model is configured to jointly source and channel decode the sequence information to obtain the measurement results.

[0203] In some embodiments, the receiving module 1010 is configured to obtain the sequence information after performing radio frequency receiving, resource demapping and demodulation on a signal from the first device.

[0204] In some embodiments, the decoding model is configured to jointly source and channel decode and demodulate the sequence information to obtain the measurement results.

[0205] In some embodiments, the receiving module 1010 is configured to obtain the sequence information after performing radio frequency receiving and resource demapping on a signal from the first device.

[0206] In some embodiments, the measurement results include all or part of measurement result data of at least one measurement resource.

[0207] In some embodiments, the measurement result data of each measurement resource includes an index of the measurement resource, and / or a link quality corresponding to the measurement resource.

[0208] In some embodiments, the all or part of measurement result data of the at least one measurement resource includes:

[0209] measurement result data of an optimal reference signal in a cell; or,

[0210] measurement result data of an optimal reference signal in L candidate cells, L being a positive integer; or

[0211] measurement result data of a plurality of reference signals.

[0212] In some embodiments, the sequence information does not have corresponding check bits.

[0213] In some embodiments, the decoding model is an AI or ML model.

[0214] In some embodiments, the reference signal is used for measurement related to a spatial domain transmission filter.

[0215] The technical scheme provided by the embodiments of the present application realizes a measurement and reporting mechanism based on a double-end encoding and decoding model based on an encoding model of a first device and a decoding model of a second device. The first device encodes the measurement result based on the encoding model and sends it to the second device, and the second device obtains the measurement result after decoding based on the decoding model. The encoding model encodes the measurement result at the sending end, compresses the measurement result to be sent, and the decoding model decodes the received signal at the receiving end to restore the measurement result, thereby reducing the overhead of the first device sending the measurement result.

[0216] It should be noted that, in actual application, the functions of the above-mentioned embodiments can be completed by different functional modules according to actual needs, that is, the content structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0217] As to the device in the above-mentioned embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0218] Please refer to FIG. 11, which shows a structural schematic diagram of a communication device provided by an embodiment of the present application. The communication device can be the first device or the second device introduced above. The communication device 1100 can include a processor 1101, a transceiver 1102, and a memory 1103. The transceiver 1102 is used to implement the sending or receiving function, such as the function of the sending module 920 or the function of the receiving module 1010. The processor 1101 can be used to implement other processing functions or control the sending and / or receiving, such as the function of the processing module 910 or the function of the processing module 1020.

[0219] The processor 1101 includes one or more processing cores, and performs various function applications and information processing by running software programs and modules.

[0220] The transceiver 1102 can include a receiver and a transmitter, which can be implemented as the same wireless communication component, and can include a wireless communication chip and a radio frequency antenna.

[0221] The memory 1103 can be connected to the processor 1101 and the transceiver 1102.

[0222] The memory 1103 can be used to store a computer program executed by the processor 1101, and the processor 1101 is configured to execute the computer program to implement various steps in the above method embodiments.

[0223] In some embodiments, when the communication device 1100 is a first device, the processor 910 is configured to input the measurement result of the reference signal as an input of an encoding model, and output sequence information by the encoding model. The transceiver 1102 is configured to send the sequence information to a second device.

[0224] In some embodiments, when the communication device 1100 is a second device, the transceiver 1102 is configured to receive sequence information sent by a first device. The processor 1101 is configured to input the sequence information as an input of a decoding model, and output a measurement result of a reference signal by the decoding model.

[0225] For details not described in the present embodiment, refer to the above embodiments, which will not be repeated here.

[0226] In addition, the memory can be implemented by any type of volatile or non-volatile storage devices or a combination thereof, including but not limited to: magnetic or optical disks, electrically erasable programmable read-only memories, erasable programmable read-only memories, static random access memories, read-only memories, magnetic memories, flash memories, programmable read-only memories.

[0227] The embodiment of the present application further provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to be executed by a processor to implement the terminal device side measurement result sending method, the terminal device side measurement result receiving method, the network device side measurement result sending method, or the network device side measurement result receiving method. Optionally, the computer readable storage medium can include a ROM (Read-Only Memory), a RAM (Random-Access Memory), a SSD (Solid State Drives), an optical disc or the like. The RAM can include a ReRAM (Resistance Random Access Memory) and a DRAM (Dynamic Random Access Memory).

[0228] The embodiment of the present application further provides a chip, wherein the chip includes a programmable logic circuit and / or program instructions, and when the chip is running, the chip is used to implement the first device side measurement result sending method or the second device side measurement result receiving method.

[0229] The embodiment of the present application further provides a computer program product, wherein the computer program product includes a computer program, the computer program is stored in a computer readable storage medium, and a processor reads and executes the computer program from the computer readable storage medium to implement the first device side measurement result sending method or the second device side measurement result receiving method.

[0230] It should be understood that the "indication" mentioned in the embodiments of the present application can be direct indication, or indirect indication, or can be an indication having a correlation relationship. For example, A indicates B, which can mean that B can be obtained by A directly; or A indirectly indicates B, for example, A indicates C, and B can be obtained by C; or A and B have a correlation relationship.

[0231] In the description of the embodiments of the present application, the term "corresponding" can mean that there is a direct or indirect corresponding relationship between the two, or can mean that there is a correlation relationship between the two, or can mean an indication and being indicated, configuration and being configured, and the like.

[0232] In some embodiments of the present application, "predefined" can be implemented by pre-storing corresponding codes, tables or other information indicating manners in devices (for example, including terminal devices and APs), and the specific implementation manners of the present application are not limited. For example, the pre-defined can mean defined in a protocol.

[0233] In some embodiments of the present application, the "protocol" can refer to a standard protocol in the communication field, which can include the LTE protocol, the NR protocol, and related protocols applied in future communication systems, and the present application is not limited thereto.

[0234] "Multiple" mentioned in the present application refers to two or more. "And / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents that the associated objects before and after it are in an "or" relationship.

[0235] "Greater than or equal to" mentioned in the present application can mean greater than or equal to, and "less than or equal to" can mean less than or equal to.

[0236] In addition, the step numbers described in the present application only exemplarily show a possible execution order between the steps, and in some other embodiments, the above steps can also be executed in a sequence different from the number, such as two different numbered steps are executed simultaneously, or two different numbered steps are executed in an order opposite to the illustration, and the embodiments of the present application are not limited thereto.

[0237] Those skilled in the art should realize that in one or more of the above examples, the functions described in the embodiments of the present application can be realized by hardware, software, firmware or any combination thereof. When realized by software, these functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium. The computer readable medium includes computer storage medium and communication medium, wherein the communication medium includes any medium facilitating the transmission of computer programs from one place to another. The storage medium can be any available medium accessible by a general or special purpose computer.

[0238] The above only describes exemplary embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for sending a measurement result, characterized in that: The method is performed by a first device, and includes: Using the measurement result of the reference signal as input to a coding model, and having the coding model output sequence information; The sequence information is sent to the second device.

2. The method according to claim 1, characterized in that The coding model is used to perform source coding on the measurement result.

3. The method according to claim 2, characterized in that The sending the sequence information to the second device includes: After performing channel coding, modulation, resource mapping, and antenna mapping on the sequence information, the sequence information is sent to the second device.

4. The method according to claim 1, wherein The coding model is used to perform joint source-channel coding on the measurement result.

5. The method according to claim 4, characterized in that The sending the sequence information to the second device includes: After performing modulation, resource mapping, and antenna mapping on the sequence information, the sequence information is sent to the second device.

6. The method according to claim 1, characterized in that The coding model is used to perform joint source-channel coding and modulation on the measurement result.

7. The method according to claim 6, characterized in that The sending the sequence information to the second device includes: After performing resource mapping and antenna mapping on the sequence information, the sequence information is sent to the second device.

8. The method according to any one of claims 1 to 7, characterized in that The measurement result includes: all or part of measurement result data of at least one measurement resource.

9. The method according to claim 8, characterized in that The measurement result data of each measurement resource includes: an index of the measurement resource, and / or a link quality corresponding to the measurement resource.

10. The method according to claim 8 or 9, characterized in that All or part of the measurement result data of the at least one measurement resource includes: Measurement result data of the best reference signal in a cell; or The measurement result data of the best reference signal in L candidate cells, where L is a positive integer; or Measurement result data of multiple reference signals.

11. The method according to any one of claims 1 to 10, characterized in that The sequence information does not have a corresponding check bit.

12. The method according to any one of claims 1 to 11, characterized in that The coding model is an artificial intelligence AI or machine learning ML model.

13. The method according to any one of claims 1 to 12, characterized in that The reference signal is used for spatial domain transmission filter related measurements.

14. A method for receiving a measurement result, characterized in that: The method is performed by a second device, and includes: receiving sequence information sent by the first device; The sequence information is used as input to a decoding model, and the decoding model outputs a measurement result of a reference signal.

15. The method according to claim 14, characterized in that The decoding model is used to perform source decoding on the sequence information to obtain the measurement result.

16. The method according to claim 15, characterized in that The receiving sequence information sent by the first device includes: The sequence information is obtained after performing radio frequency reception, resource demapping, demodulation, and channel decoding on the signal from the first device.

17. The method according to claim 14, characterized in that The decoding model is used to perform joint source-channel decoding on the sequence information to obtain the measurement result.

18. The method according to claim 17, characterized in that The receiving sequence information sent by the first device includes: The sequence information is obtained after performing radio frequency reception, resource demapping, and demodulation on the signal from the first device.

19. The method according to claim 14, wherein The decoding model is used to perform joint source-channel decoding and demodulation on the sequence information to obtain the measurement result.

20. The method according to claim 19, wherein The receiving sequence information sent by the first device includes: The sequence information is obtained after performing radio frequency reception and resource demapping on the signal from the first device.

21. The method according to any one of claims 14 to 20, characterized in that The measurement result includes: all or part of measurement result data of at least one measurement resource.

22. The method according to claim 21, characterized in that The measurement result data of each measurement resource includes: an index of the measurement resource, and / or a link quality corresponding to the measurement resource.

23. The method according to claim 21 or 22, characterized in that All or part of the measurement result data of the at least one measurement resource includes: Measurement result data of the best reference signal in a cell; or The measurement result data of the best reference signal in L candidate cells, where L is a positive integer; or Measurement result data of multiple reference signals.

24. The method according to any one of claims 14 to 23, characterized in that The sequence information does not have a corresponding check bit.

25. The method according to any one of claims 14 to 24, characterized in that The decoding model is an artificial intelligence AI or machine learning ML model.

26. The method according to any one of claims 14 to 25, characterized in that The reference signal is used for spatial domain transmission filter related measurements.

27. A device for sending measurement results, characterized in that: The device comprises: a processing module, configured to use the measurement result of the reference signal as an input to a coding model, and the coding model outputs sequence information; A sending module is configured to send the sequence information to the second device.

28. A device for receiving measurement results, characterized in that: The device comprises: A receiving module, configured to receive sequence information sent by the first device; The processing module is configured to use the sequence information as an input to a decoding model, and the decoding model outputs a measurement result of a reference signal.

29. A communication device, characterized in that: The communication device includes a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method according to any one of claims 1 to 13, or implements the method according to any one of claims 14 to 26.

30. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which is used to be executed by a processor to implement the method according to any one of claims 1 to 13, or to implement the method according to any one of claims 14 to 26.

31. A chip, characterized in that: The chip includes a programmable logic circuit and / or program instructions, and when the chip is running, is used to implement the method according to any one of claims 1 to 13, or to implement the method according to any one of claims 14 to 26.

32. A computer program product, characterized in that The computer program product includes computer instructions, which are stored in a computer-readable storage medium. A processor reads and executes the computer instructions from the computer-readable storage medium to implement the method according to any one of claims 1 to 13, or implements the method according to any one of claims 14 to 26.

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