Abstract: In accordance with an embodiment, a method includes encoding channel information of N downlink transport layers using a first encoder to determine N pieces of first channel state indication information wherein N is a positive integer greater than 1; encoding the N pieces of first channel state indication information using a second encoder to determine second channel state indication information, wherein the second channel state indication information corresponds to the N downlink transport layers, and a sequence length corresponding to the second channel state indication information is less than a sum of sequence lengths corresponding to the N pieces of first channel state indication information; and sending the second channel state indication information.

Abstract: A model application method and an apparatus are provided. The method includes: determining first information based on a received reference signal, where the first information includes data of m ports for the reference signal; and mapping the data of the m ports to an input of a first model based on a correspondence between the m ports and n ports in an input dimension of the first model, to obtain an output of the first model, where both m and n are integers greater than or equal to 1, and m and n are not equal. By using the method and the apparatus in this application, self-adaptation to a port of the reference signal can be implemented, and model storage and training overheads can be reduced.

Abstract: In accordance with an embodiment, a method includes: obtaining a first manner, where the first manner indicates a first downlink reference signal that is among received downlink reference signals and that is used to obtain first channel environment information, the first channel environment information is used as an input of a first artificial intelligence (AI) model, and the first channel environment information is current channel environment information; generating a first uplink signal at an output of the first AI model; and sending the first uplink signal.

Abstract: A random access method and an apparatus. In the method, random access is completed in a manner in which an agent terminal performs access, so that an access requirement of a terminal can be satisfied under low energy consumption. The method and the apparatus may be applied to application scenarios such as an internet of things (IoT), machine type communication (MTC), massive machine type communication (mMTC), an internet of vehicles, and a connected vehicle.

Abstract: A reference signal indication method includes receiving a plurality of reference signals, and sending at least one codeword on a first time-frequency resource. The at least one codeword includes a first codeword. The first codeword corresponds to a first reference signal in N reference signals of the plurality of reference signals. The first reference signal is a selected reference signal. The N reference signals are a subset of the plurality of reference signals, and N is a positive integer.

Abstract: A method includes: When user equipment is in an RRC idle mode or inactive mode, the user equipment sends a first random access preamble to an access network device on a PRACH resource, and sends first uplink data to the access network device on a PUSCH resource. The user equipment sends, by using at least two beams respectively, SRSs to the access network device on at least two SRS resources associated with the PUSCH resource. The user equipment receives a random access response from the access network device, where the random access response includes beam indication information, and the beam indication information is used to indicate an optimal beam in the at least two beams.

Abstract: This disclosure provides a communication method and a communication apparatus. The method includes: receiving first information from a first node, where the first information is for configuring a first model structure of an intelligent model, the first model structure includes N operations, N is a positive integer, an operation in the N operations is a calculation for obtaining an output parameter by using an operation parameter and an operator, and the operator is an operation rule or a function; and determining the intelligent model based on the first information. This enables communication nodes to reach a consensus on the model structure, makes the intelligent model applicable to a communication network, and implements communication network intelligence.

Abstract: An uplink precoding method and an apparatus are provided. The method includes: A terminal device receives first information from an access network device; the terminal device determines a first precoding matrix based on the first information and a first model, where the first precoding matrix is determined based on an output of the first model, and an input of the first model is determined based on the first information; the terminal device precodes an uplink signal based on the first precoding matrix; and the terminal device sends a precoded uplink signal to the access network device. In the method, the terminal device determines the first precoding matrix based on the first information and the first model. The precoding matrix is no longer selected from an offline codebook, but is a floating-point matrix generated by the terminal device based on the first information.

Abstract: A data processing method for a model and an apparatus are provided. The method includes: A first node determines the data processing method for the model; and the first node implements, according to the data processing method for the model, at least one of the following: performing model training or performing model inference. Based on the method and the apparatus, inference tasks for different radio resource configurations can be completed by using one model, so that overheads for model training and storage are reduced.

Abstract: This disclosure provides a communication method and apparatus. An apparatus obtains an ith piece of first information that indicates an ith local model of each of K subnodes and i is a positive integer, and determines an ith federated learning model of a kth subnode based on the ith piece of first information and a type of the ith federated learning model of the kth subnode, where k is any positive integer from 1 to K. When i is greater than 1, the type of the ith federated learning model of the kth subnode is determined based on a type or performance of an (i?1)th federated learning model of the kth subnode. The apparatus sends an ith piece of second information, where the ith piece of second information indicates an ith federated learning model of each of the K subnodes.

Abstract: This disclosure provides a communication method and apparatus, to reduce transmission overheads of training data and improve performance of an artificial intelligence model. The method includes: obtaining first data and first information from a first node, where the first information indicates a data augmentation manner of the first data; and determining a first training dataset of a model based on the first data and the first information.

Abstract: A communication method and a communication apparatus. The method includes: the communication apparatus receives first information, where the first information indicates a training policy of a first intelligent model. The communication apparatus performs model training on the first intelligent model according to the training policy. In this way, an intelligent model that meets a communication performance requirement can be obtained while air interface resource overheads are reduced.

Abstract: This application provides a calibration method and apparatus. The method includes: determining first channel state information and second channel state information, where the first channel state information is obtained through prediction via a first channel prediction model, and the second channel state information is determined based on channel estimation on a downlink signal from an access network device; and sending first information to the access network device if a difference metric value between the first channel state information and the second channel state information is greater than or equal to a first threshold, where the first information indicates the second channel state information.

Abstract: This application provides a communication method and apparatus. The method includes: determining a first downlink signal and a second downlink signal, where the first downlink signal is a downlink signal determined based on an output of a first prediction model, an input of the first prediction model is determined based on Y pieces of downlink measurement information in X pieces of downlink measurement information, and the second downlink signal is determined based on the X pieces of downlink measurement information; and indicating the second downlink signal and the Y pieces of downlink measurement information to an access network device if the first downlink signal and the second downlink signal meet a first condition. According to the method, the first prediction model can be updated based on the foregoing information, and performance of the prediction model can be continuously improved.

Abstract: This application provides a data processing method and a communication apparatus. The method includes: receiving or sending a plurality of reference signals; obtaining a first channel data set, where the first channel data set is a set of a plurality of pieces of channel data obtained based on the plurality of reference signals; obtaining a data quality screening policy, where the data quality screening policy is used to obtain, through screening, model training data that meets a data quality requirement; and screening the channel data in the first channel data set according to the data quality screening policy, to obtain a training data set. In this way, quality of the training data for model training is improved, so that a trained intelligent model meets a performance requirement.

Abstract: A communication method and apparatus are provided. The method includes: obtaining N pieces of training data, where for one of the N pieces of training data, the training data corresponds to one piece of mark information, and the mark information indicates an attribute of the training data; and sending indication information to a terminal device, where the indication information indicates information about M artificial intelligence models, the artificial intelligence model is trained based on X pieces of training data in the N pieces of training data, and the X pieces of training data are determined based on the mark information. When the N pieces of training data are obtained, the N pieces of training data may be used for constructing training data corresponding to different mark information, to train the M artificial intelligence models or more artificial intelligence models, so that scenario-based artificial intelligence model training can be implemented.

Abstract: A random access method, a device, and a system are provided, related to the field of communications technologies.

Abstract: This application provides an artificial intelligence model download method, an apparatus, and a system. The method includes: A terminal device receives model feedback information from an access network device, where the model feedback information indicates a download mode that is of a first artificial intelligence model and that is configured for the terminal device, and the download mode that is of the first artificial intelligence model and that is configured for the terminal device is a download mode supported by the terminal device. The terminal device may download the first artificial intelligence model in the download mode indicated by the model feedback information. The terminal device can download the first artificial intelligence model supported by the terminal device, to avoid a case in which a downloaded artificial intelligence model cannot be used.

Abstract: A communication method and apparatus. A terminal receives first information from a network device. The terminal determines N pieces of training data based on the first information, and N is an integer. The terminal performs model training based on the N pieces of training data, to obtain a first AI model. The network device configures the first information used to determine the N pieces of training data for the terminal. The terminal performs model training based on the N pieces of training data autonomously. Separately configuring an AI model for the terminal is not necessary and air interface overheads are reduced.

Abstract: A communication method includes determining a first submodel and a second submodel. The first submodel and the second submodel are used in a matching manner. The communication method also includes sending first information. The first information indicates one or more of input data of the first submodel or output data of the first submodel.