WO2024088162A1 - 信息传输方法、信息处理方法、装置和通信设备 - Google Patents

信息传输方法、信息处理方法、装置和通信设备 Download PDF

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Publication number
WO2024088162A1
WO2024088162A1 PCT/CN2023/125561 CN2023125561W WO2024088162A1 WO 2024088162 A1 WO2024088162 A1 WO 2024088162A1 CN 2023125561 W CN2023125561 W CN 2023125561W WO 2024088162 A1 WO2024088162 A1 WO 2024088162A1
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Prior art keywords
information
csi
groups
channel
channel information
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PCT/CN2023/125561
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English (en)
French (fr)
Inventor
任千尧
吴昊
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维沃移动通信有限公司
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Publication of WO2024088162A1 publication Critical patent/WO2024088162A1/zh

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signaling, i.e. of overhead other than pilot signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present application belongs to the field of communication technology, and specifically relates to an information transmission method, an information processing method, an apparatus and a communication device.
  • the AI network model may include an encoding part (i.e., an encoding AI network model) and a decoding part (i.e., a decoding AI network model).
  • the encoding AI network model is used to encode channel information into channel characteristic information
  • the decoding AI network model is used to restore the channel characteristic information output by the encoding AI network model into channel information.
  • the input dimension of the same AI network model is fixed.
  • Different AI network models are required for channel information with different numbers of Channel State Information-Reference Signal (CSI-RS) ports.
  • CSI-RS Channel State Information-Reference Signal
  • the AI network model trained for 8 CSI-RS ports cannot be used in a channel with 16 CSI-RS ports, it is necessary to train and transmit AI network models that match the number of CSI-RS ports. This will increase the computational complexity of training the AI network models that match the number of CSI-RS ports, and increase the overhead of transmitting the AI network models that match the number of CSI-RS ports.
  • the embodiments of the present application provide an information transmission method, an information processing method, an apparatus, and a communication device, so that an AI network model with a low number of CSI-RS ports can process channel information with a high number of CSI-RS ports, thereby improving the multiplexing efficiency and flexibility of the AI network model.
  • a method for transmitting information comprising:
  • the terminal determines, based on the first information, K groups of second channel information from the first channel information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1;
  • the terminal performs a first AI network model on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information.
  • the information is first processed to obtain M channel characteristic information, and the K groups of second channel information include the M groups of second channel information;
  • the terminal sends second information to the network side device, where the second information includes the M channel characteristic information.
  • an information transmission device which is applied to a terminal, and the device includes:
  • a first determination module configured to determine K groups of second channel information from the first channel information based on the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond to the K groups of CSI-RS transmission ports in one-to-one correspondence, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1;
  • a first processing module configured to perform a first processing on the M groups of second channel information based on the first AI network models corresponding to the M groups of second channel information, to obtain M channel feature information, wherein the K groups of second channel information include the M groups of second channel information;
  • the first sending module is used to send second information to the network side device, where the second information includes the M channel characteristic information.
  • an information processing method comprising:
  • the network side device receives second information from the terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information;
  • the network side device determines, according to the first information, a second AI network side model corresponding to each of the M channel characteristic information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, the K groups of second channel information include the M groups of second channel information, and K and M are integers greater than or equal to 1;
  • the network side device performs a second processing on the M channel characteristic information based on the second AI network side models corresponding to each of the M channel characteristic information to obtain the M groups of second channel information.
  • an information processing device which is applied to a network side device, and the device includes:
  • a first receiving module configured to receive second information from a terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information;
  • a second determination module configured to determine, according to the first information, a second AI network-side model corresponding to each of the M channel characteristic information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, the K groups of second channel information include the M groups of second channel information, and K and M are integers greater than or equal to 1;
  • the second processing module is used to perform a second processing on the M channel characteristic information based on the second AI network side model corresponding to each of the M channel characteristic information to obtain the M groups of second channel information.
  • a communication device which includes a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect or the third aspect are implemented.
  • a terminal comprising a processor and a communication interface, wherein the processor is used to determine K groups of second channel information from the first channel information based on the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1; the processor is also used to perform a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel characteristic information, the K groups of second channel information include the M groups of second channel information; the communication interface is used to send second information to a network side device, and the second information includes the M channel characteristic information.
  • a network side device including a processor and a communication interface, wherein the communication interface is used to receive second information from a terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information; the processor is used to determine the second AI network side model corresponding to each of the M channel characteristic information according to the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, the K groups of second channel information include the M groups of second channel information, and K and M are integers greater than or equal to 1; the processor is also used to perform a second processing on the M channel characteristic information based
  • a communication system comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the information transmission method as described in the first aspect, and the network side device can be used to execute the steps of the information processing method as described in the third aspect.
  • a readable storage medium on which a program or instruction is stored.
  • the program or instruction is executed by a processor, the steps of the information transmission method described in the first aspect are implemented, or the steps of the information processing method described in the third aspect are implemented.
  • a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the information transmission method as described in the first aspect, or to implement the information processing method as described in the third aspect.
  • a computer program/program product is provided, wherein the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the steps of the information transmission method as described in the first aspect, or the computer program/program product is executed by at least one processor to implement the steps of the information processing method as described in the third aspect.
  • the CSI-RS transmission ports are grouped, and the channel information of each group of CSI-RS transmission ports is The information is processed by a corresponding AI network model, wherein the first channel information of a channel can be divided into K groups, and each AI network model only inputs the channel information of a corresponding group of CSI-RS transmission ports, without inputting the channel information of all CSI-RS transmission ports.
  • the AI network model with a low number of CSI-RS transmission ports can be used to process the channel information of a high number of CSI-RS transmission ports, thereby improving the multiplexing efficiency and flexibility of the AI network model.
  • FIG1 is a schematic diagram of the structure of a wireless communication system to which an embodiment of the present application can be applied;
  • FIG2 is a flow chart of an information transmission method provided in an embodiment of the present application.
  • FIG3 is a flow chart of an information processing method provided in an embodiment of the present application.
  • FIG4 is a schematic diagram of the structure of an information transmission device provided in an embodiment of the present application.
  • FIG5 is a schematic diagram of the structure of an information processing device provided in an embodiment of the present application.
  • FIG6 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of the hardware structure of a terminal provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a network side device provided in an embodiment of the present application.
  • first, second, etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by “first” and “second” are generally of the same type, and the number of objects is not limited.
  • the first object can be one or more.
  • “and/or” in the specification and claims represents at least one of the connected objects, and the character “/" generally represents that the objects associated with each other are in an "or” relationship.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency Division Multiple Access
  • NR new radio
  • FIG1 shows a block diagram of a wireless communication system applicable to an embodiment of the present application.
  • the wireless communication system includes a terminal 11 and a network side device 12.
  • the terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) device , robots, wearable devices (Wearable Device), vehicle user equipment (VUE), pedestrian user equipment (PUE), smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), game consoles, personal computers (personal computers, PCs), teller machines or self-service machines and other terminal side devices, wearable devices include: smart watches, smart bracelets, smart headphones,
  • the network side device 12 may include access network equipment or core network equipment, wherein the access network equipment may also be called wireless access network equipment, wireless access network (Radio Access Network, RAN), wireless access network function or wireless access network unit.
  • the access network equipment may include a base station, a wireless local area network (WLAN) access point or a WiFi node, etc.
  • WLAN wireless local area network
  • the base station may be called a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home node B, a home evolved node B, a transmitting and receiving point (TRP) or some other suitable term in the field.
  • eNB evolved node B
  • BTS base transceiver station
  • ESS extended service set
  • TRP transmitting and receiving point
  • the base station is not limited to specific technical vocabulary. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.
  • the transmitter can optimize the signal transmission based on CSI to make it more compatible with the channel state.
  • the channel quality indicator CQI
  • MCS modulation and coding scheme
  • PMI precoding matrix indicator
  • MIMO multi-input multi-output
  • the base station sends a CSI Reference Signal (CSI-RS) on certain time-frequency resources in a certain time slot.
  • CSI-RS CSI Reference Signal
  • the terminal performs channel estimation based on the CSI-RS, calculates the channel information on this slot, and feeds back the PMI to the base station through the codebook.
  • the base station combines the channel information based on the codebook information fed back by the terminal, and uses this to perform data precoding and multi-user scheduling before the next CSI report.
  • the terminal can change the PMI reported for each subband to reporting PMI according to delay. Since the channels in the delay domain are more concentrated, PMIs with less delay can approximately represent all subbands. The PMI of the delay domain is compressed before reporting.
  • the base station can pre-code the CSI-RS in advance and send the encoded CSI-RS to the terminal.
  • the terminal sees the channel corresponding to the encoded CSI-RS.
  • the terminal only needs to select several ports with higher strength from the ports indicated by the network side and report the coefficients corresponding to these ports.
  • neural network or machine learning methods can be used.
  • AI modules such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application uses neural networks as an example for illustration, but does not limit the specific type of AI modules.
  • the parameters of the neural network are optimized through optimization algorithms.
  • An optimization algorithm is a type of algorithm that can help us minimize or maximize an objective function (sometimes called a loss function).
  • the objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we build a neural network model f(.). With the model, we can get the predicted output f(x) based on the input x, and we can calculate the difference between the predicted value and the true value (f(x)-Y), which is the loss function. Our goal is to find the right weights and biases to minimize the value of the above loss function. The smaller the loss value, the closer our model is to the real situation.
  • the common optimization algorithms are basically based on the error back propagation (BP) algorithm.
  • BP error back propagation
  • the basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of the signal and the back propagation of the error.
  • the input sample is transmitted from the input layer, processed by each hidden layer layer by layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error back propagation stage.
  • Error back propagation is to propagate the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all units in each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the basis for correcting the weights of each unit.
  • This process of adjusting the weights of each layer of the signal forward propagation and error back propagation is repeated.
  • the process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until the pre-set number of learning times is reached.
  • the CSI compression recovery process is as follows: the terminal estimates the CSI-RS, calculates the channel information, obtains the encoding result of the calculated channel information or the original estimated channel information through the encoding AI network model, and sends the encoding result to the base station.
  • the base station receives the encoded result and inputs it into the decoding AI network model to recover the channel information.
  • the CSI compression feedback scheme based on neural network is to compress and encode the channel information at the terminal.
  • the compressed content is sent to the base station, where it is decoded to restore the channel information.
  • the decoding AI network model of the base station and the encoding AI network model of the terminal need to be jointly trained to achieve a reasonable match.
  • the input of the encoding AI network model is channel information
  • the output is encoding information, that is, channel feature information.
  • the input of the decoding AI network model is encoding information, and the output is restored channel information.
  • the channel information input to the coding AI network model is the channel matrix or precoding matrix of each subband.
  • the precoding matrix is the rank number, that is, the total number of layers, and the number of rows of the precoding matrix is the number of CSI-RS ports.
  • the input dimension of the coding AI network model is determined by the number of ranks, the number of CSI-RS ports, and the number of subbands.
  • the channel information of each channel is processed by a coding AI network model. When the number of CSI-RS ports of the channel changes, the channel information of the channel no longer matches the coding AI network model used before the number of CSI-RS ports changes.
  • CSI-RS transmitting ports are grouped, and channel information corresponding to at least one CSI-RS transmitting port in a group is processed using an AI network model, so that an AI network model with a low number of CSI-RS transmitting ports can process channel information with a high number of CSI-RS transmitting ports, and the number of AI network models is reduced, as well as the size of the AI network model.
  • a channel is configured with 32 CSI-RS transmission ports, which are divided into 4 groups, each with 8 CSI-RS transmission ports, then an AI network model only needs to process the channel information of 8 CSI-RS transmission ports.
  • the number of CSI-RS transmission ports of the channel is changed to 16, it is divided into 2 groups, each with 8 CSI-RS transmission ports.
  • the same AI network model can be reused to process the channel information of the 2 groups of CSI-RS transmission ports.
  • an information transmission method provided in an embodiment of the present application the execution subject of which is a terminal.
  • the information transmission method executed by the terminal may include the following steps:
  • Step 201 The terminal determines K groups of second channel information from the first channel information based on the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1.
  • the first channel information can be the original channel matrix or precoding matrix of a certain channel, or the preprocessed channel matrix or precoding matrix.
  • the embodiments of the present application are usually illustrated by taking the channel information as a precoding matrix, which does not constitute a specific limitation here.
  • a set of second channel information includes at least one of the following:
  • a matrix consisting of elements in a first precoding matrix corresponding to the group of CSI-RS transmission ports, wherein the first precoding matrix is a precoding matrix for the first channel information;
  • a second precoding matrix where the second precoding matrix is a precoding matrix for the set of second channel information.
  • the first precoding matrix may be a precoding matrix determined based on the first channel information as a whole
  • the second precoding matrix may be a precoding matrix determined based on a group of second channel information as a whole.
  • any two groups of second channel information in the K groups of second channel information may partially overlap. For example, assuming that the target channel includes 32 CSI-RS transmission ports, K is equal to 3, then the first group of second channel information includes the 0th to 11th CSI-RS transmission ports, the second group of second channel information includes the 12th to 21st CSI-RS transmission ports, and the third group of second channel information includes the 20th to 31st CSI-RS transmission ports.
  • any two groups of second channel information in the K groups of second channel information do not overlap.
  • the K groups of second channel information have the same length.
  • the lengths of K groups of second channel information may be different. For example, assuming K is equal to 2, one group of second channel information includes channel information of 4 CSI-RS transmission ports, and the other includes channel information of 8 CSI-RS transmission ports.
  • Step 202 The terminal performs a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel characteristic information, and the K groups of second channel information include the M groups of second channel information.
  • the first AI network model may be a coding AI network model
  • the first processing may include: at least one of: encoding, compression, quantization and other processing.
  • the M groups of second channel information may correspond to the same first AI network model.
  • the lengths of the M groups of second channel information are the same or different.
  • zero padding may be used to adjust the second channel information to have the same length, and the adjusted channel information of the same length may be processed using the same first AI network model.
  • the first AI network models corresponding to the M groups of second channel information are different from each other.
  • a portion of the M groups of second channel information corresponds to the same first AI network model, and another portion of the M groups of second channel information corresponds to different first AI network models.
  • Step 203 The terminal sends second information to the network side device, where the second information includes the M channel characteristic information.
  • the terminal reports M channel characteristic information via a CSI report.
  • the network side device when receiving M channel feature information, performs second processing on the M channel feature information based on the second AI network side model corresponding to each of the M channel feature information to restore the M groups of second channel information.
  • the second processing may be at least one of decoding, decompression, dequantization, and the like.
  • the first information includes at least one of the following:
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports is a group of CSI-RS transmission ports.
  • Option 1 The value of K is used to indicate how many groups all CSI-RS transmission ports of the target channel are divided into.
  • the terminal may evenly divide all CSI-RS transmission ports of the target channel into K groups. For example, assuming that the target channel includes X CSI-RS transmission ports, the base station indicates or the protocol agrees on the value of K, then the terminal determines that each group of CSI-RS transmission ports includes X ⁇ K CSI-RS transmission ports.
  • the handling method for the non-divisible may be agreed upon by the protocol, indicated by the base station, or determined and reported by the terminal. For example, assuming that X is equal to 16 and K is equal to 5, the terminal may divide the X CSI-RS transmission ports into 5 groups, each group includes 3 CSI-RS transmission ports, and the remaining 1 CSI-RS transmission port may not be reported.
  • the terminal may divide all CSI-RS transmission ports of the target channel into K groups with different lengths.
  • the number of CSI-RS transmission ports in each group of CSI-RS transmission ports may be: the first group of CSI-RS transmission ports includes A1 CSI-RS transmission ports, the second group of CSI-RS transmission ports includes A2 CSI-RS transmission ports, ..., the Kth group of CSI-RS transmission ports includes Ak CSI-RS transmission ports. In this way, the terminal can determine the number of CSI-RS transmission ports included in each of the K groups of CSI-RS transmission ports.
  • the CSI-RS transmission ports within a group of CSI-RS transmission ports may be CSI-RS transmission ports that are arranged consecutively.
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports may be: determine which one or which CSI-RS transmission ports are specifically included in each group of CSI-RS transmission ports, wherein the identifier of the CSI-RS transmission port may be a number, such as the arrangement order of the CSI-RS transmission ports.
  • the first group of CSI-RS transmission ports includes CSI-RS transmission ports 1, 3, and 5; the second group of CSI-RS transmission ports includes CSI-RS transmission ports 2, 4, and 6.
  • the CSI-RS transmission ports within a group of CSI-RS transmission ports may be non-continuously arranged CSI-RS transmission ports.
  • the first information satisfies at least one of the following:
  • the network side device may indicate the grouping information of the CSI-RS transmission port through signaling, for example: including the number of groups K, or the number of CSI-RS transmission ports in each group of CSI-RS transmission ports, or which CSI-RS transmission ports are specifically included in each group of CSI-RS transmission ports.
  • the network side device may configure the grouping information of the CSI-RS sending port in the CSI report configuration (report config).
  • the terminal may determine a port grouping method according to an input dimension of the first AI network model possessed by the terminal, so that each group of second channel information after grouping matches the input dimension of the first AI network model possessed by the terminal.
  • the terminal may report the first information to the network side device.
  • the grouping information of the CSI-RS transmission ports may be agreed upon by a protocol.
  • the protocol stipulates that the CSI-RS transmission ports in the same group meet at least one of the following conditions:
  • the polarization directions are the same, that is, among the CSI-RS transmission ports, the CSI-RS transmission ports in the first polarization direction are divided into one group, and the CSI-RS transmission ports in the second polarization direction are divided into another group, for example: the first polarization direction is the horizontal polarization direction, and the second polarization direction is the vertical polarization direction; or, the first polarization direction is the +45 degree polarization direction, and the second polarization direction is the -45 degree polarization direction, which is not exhaustive here;
  • the corresponding transmitting antennas are located in the same row, that is, the transmitting antennas corresponding to the CSI-RS transmitting ports in a group are located in the same row on the antenna panel of the network side device;
  • the corresponding transmitting antennas are located in the same column, that is, the transmitting antennas corresponding to the CSI-RS transmitting ports in a group are located in the same column on the antenna panel of the network side device;
  • the time domain resources are the same;
  • the frequency domain resources are the same;
  • CDM code division multiplexing
  • N The N CSI-RS transmission ports that are consecutively arranged among all the CSI-RS transmission ports that include the first channel information, where N is an integer greater than or equal to 1.
  • a group of CSI-RS transmission ports includes CSI-RS transmission ports of different CDMs corresponding to the same time-frequency resource, or a group of CSI-RS transmission ports includes CSI-RS transmission ports on all time-frequency resources corresponding to the same CDM.
  • the CSI-RS transmission ports in the same group are N CSI-RS transmission ports arranged continuously
  • the CSI-RS transmission ports in the first group of CSI-RS transmission ports and the CSI-RS transmission ports in the second group of CSI-RS transmission ports may be arranged sequentially or in reverse order
  • the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports are two adjacent groups of CSI-RS transmission ports in the K groups of CSI-RS transmission ports.
  • the first group of CSI-RS transmission ports includes the 0th to 9th CSI-RS transmission ports
  • the second group of CSI-RS transmission ports includes the 10th to 16th CSI-RS transmission ports.
  • the network side device may indicate the value of K
  • the protocol may stipulate that the polarization directions of the CSI-RS transmission ports in the same group are the same
  • the terminal may determine the number of CSI-RS transmission ports included in each group of CSI-RS transmission ports, and which CSI-RS transmission ports are specifically included, based on K indicated by the network side device and the polarization directions of the CSI-RS transmission ports in the same group stipulated by the protocol.
  • the information transmission method further includes:
  • the terminal determines the first information according to the first AI network side model
  • the terminal sends first indication information to the network side device, where the first indication information indicates the first information.
  • the terminal after the terminal determines the first information, it reports the first information to the network side device.
  • the network side device can determine, based on the received first information, the channel information of which CSI-RS transmitting ports each channel characteristic information is based on, thereby restoring the channel information of these CSI-RS transmitting ports.
  • the terminal can determine the first information according to the input dimension of the first AI network side model so that the dimension of each group of second channel information divided according to the first information matches the input dimension of the first AI network side model.
  • the information transmission method further includes:
  • the terminal receives third information from the network side device
  • the third information indicates or configures the first information, or the third information indicates a first identifier, the first identifier is associated with the first information, and the terminal learns a first association relationship between the first information and the first identifier.
  • the terminal learns the first association relationship between the first information and the first identifier, which may be agreed upon in the protocol or the network side device configures the association relationship between various first information and its first identifier in advance.
  • the third information may be included in the CSI report config.
  • the first information may be indicated or configured by the network side device.
  • the terminal performs a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel feature information, including:
  • the terminal performs target normalization processing on the M groups of second channel information respectively to obtain M groups of third channel information
  • the terminal performs a first processing on the M groups of third channel information based on the first AI network model corresponding to each of the M groups of third channel information to obtain M channel feature information.
  • the target normalization process includes at least one of the following:
  • the maximum value normalization processing may be to divide each element in the M groups of second channel information by the element with the largest amplitude, so as to obtain M groups of third channel information after the maximum value normalization processing.
  • the maximum value normalization processing may be uniformly normalizing the M groups of second channel information.
  • an element with the largest amplitude is selected from the M groups of second channel information, and each element in the M groups of second channel information is divided by the element with the largest amplitude, respectively, to obtain M groups of third channel information after maximum value normalization processing.
  • the maximum value normalization process may be to perform normalization process on each of the M groups of second channel information respectively.
  • an element with the largest amplitude is selected from a group of second channel information, and each element in the group of second channel information is divided by the element with the largest amplitude respectively, to obtain a group of third channel information after the maximum value normalization process.
  • the power normalization processing can be to normalize the elements in the second channel information based on the power adjustment factor. Similar to the maximum value normalization processing, the power normalization processing can also be to uniformly normalize the M groups of second channel information, or to normalize each group in the M groups of second channel information separately, which will not be elaborated here.
  • the channel information input into the first AI network model is a normalized channel matrix or precoding matrix.
  • the channel information corresponding to each CSI-RS transmission port group is normalized before being input into the first AI network model.
  • a portion of the normalized precoding matrix such as a portion of the elements of a column vector of 32 elements corresponding to 32 CSI-RS transmission ports, is power normalized or maximum value normalized and input into the first AI network model.
  • the precoding matrix corresponding to each group of CSI-RS transmission ports is normalized and input into the first AI network model.
  • the channel information input into the first AI network model may be an unnormalized channel matrix or precoding matrix.
  • an unnormalized channel matrix is input into the first AI network model, or an unnormalized precoding matrix portion is input into the first AI network model.
  • the precoding matrix corresponding to 32 ports is a vector of 32 elements, and the coefficients corresponding to a group of CSI-RS transmission ports may be directly input into the first AI network model, without specific limitation herein.
  • the information transmission method further includes:
  • the terminal sends fourth information to the network side device, where the fourth information includes a phase and/or amplitude relationship between M groups of CSI-RS transmission ports corresponding to the M groups of second channel information.
  • the channel information of different groups of CSI-RS transmission ports may be normalized differently, and the terminal needs to additionally report the amplitude and/or phase difference (corresponding to the maximum value normalization processing) or the power adjustment factor (corresponding to the power normalization processing) between each group of CSI-RS transmission ports.
  • the fourth information when the second channel information is a complex number, includes the phase and amplitude relationship between the M groups of CSI-RS transmission ports corresponding to the M groups of second channel information.
  • the fourth information when the second channel information is a real number, includes amplitude relationships between M groups of CSI-RS transmission ports corresponding to the M groups of second channel information.
  • the fourth information may include an amplitude level, each amplitude level corresponds to an amplitude interval.
  • the amplitude relationship between the M groups of CSI-RS transmission ports may include an amplitude level difference between the M groups of CSI-RS transmission ports.
  • the amplitude relationship between the two groups of CSI-RS transmission ports may not be reported.
  • the second channel information is a complex number
  • the phase relationship between the two groups of CSI-RS transmission ports may be reported, but the amplitude relationship between the two groups of CSI-RS transmission ports is not reported.
  • the terminal also reports the amplitude between the channel information of each group of CSI-RS transmission ports to the network side device.
  • the network side device can restore the true value of the second channel information according to the amplitude and/or phase difference, or the power adjustment factor between the channel information of each group of CSI-RS sending ports obtained.
  • phase and/or amplitude relationship between the M groups of CSI-RS transmission ports includes:
  • the amplitude and/or phase difference between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports, the first group of CSI-RS transmission ports is the group where the strongest CSI-RS transmission port among the M groups of CSI-RS transmission ports is located or the strongest CSI-RS transmission port group among the M groups of CSI-RS transmission ports, and the second group of CSI-RS transmission ports is each group of CSI-RS transmission ports among the M groups of CSI-RS transmission ports except the first group of CSI-RS transmission ports.
  • phase and/or amplitude relationship includes at least one of the following:
  • a phase difference between a first CSI-RS transmit port and a second CSI-RS transmit port the first CSI-RS transmit port being the strongest CSI-RS transmit port in the first group of CSI-RS transmit ports, and the second CSI-RS transmit port comprising the strongest CSI-RS transmit port in the second group of CSI-RS transmit ports;
  • a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports is a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports.
  • the phase and/or amplitude relationship between the above-mentioned M groups of CSI-RS transmission ports may be a phase difference and/or an amplitude difference, for example: the first group of second channel information corresponding to the first group of CSI-RS transmission ports is subjected to maximum value normalization processing based on the strongest element W1 in the first group of second channel information, and the second group of second channel information corresponding to the second group of CSI-RS transmission ports is subjected to maximum value normalization processing based on the strongest element W2 in the second group of second channel information.
  • the phase relationship between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports includes the phase difference between W1 and W2
  • the amplitude relationship between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports includes the amplitude difference between W1 and W2.
  • the above-mentioned phase and/or amplitude relationship may include a power adjustment factor, wherein the power adjustment factor may be the ratio of the total power of a group of CSI-RS transmitting ports to the total power of the M groups of CSI-RS transmitting ports.
  • the information transmission method further includes:
  • the terminal sends an identifier of the first group of CSI-RS sending ports to the network side device.
  • the terminal informs the network side device which group of CSI-RS transmission ports is the strongest CSI-RS transmission port group, so that the network side device can restore the second channel information of other groups of CSI-RS transmission ports based on the second channel information corresponding to the strongest CSI-RS transmission port group.
  • the correspondence between the second channel information and the first AI network side model satisfies at least one of the following:
  • the M groups of second channel information correspond to the same first AI network side model
  • the network side device indicates the first AI network side model corresponding to each of the M groups of second channel information
  • the number of CSI-RS transmission ports in the CSI-RS transmission port group corresponding to the target second channel information is the same as the number of CSI-RS transmission ports in the target second channel information.
  • the input dimension of the first AI network side model corresponding to the second channel information matches, and the M groups of second channel information include the target second channel information.
  • the lengths of the M groups of second channel information are equal, that is, the M groups of CSI-RS transmission ports include the same number of CSI-RS transmission ports.
  • the M groups of second channel information correspond to the same first AI network side model. For example, assuming that the target channel includes X CSI-RS transmission ports, the base station indicates or the protocol agrees on the value of K, then the terminal determines that each group of CSI-RS transmission ports includes X ⁇ K CSI-RS transmission ports, and the channel information of each X ⁇ K CSI-RS transmission ports is taken as a group and input into the first AI network side model to obtain the channel characteristic information corresponding to the group of CSI-RS transmission ports.
  • M groups of second channel information correspond to different first AI network side models.
  • the first AI network side model corresponding to each group of second channel information can be indicated by the network side device or determined by the terminal.
  • the information transmission method further includes:
  • the terminal sends third indication information to the network side device, where the third indication information indicates a first AI network model corresponding to each of the M groups of second channel information.
  • the terminal when the terminal determines the first AI network side model corresponding to each group of second channel information, the terminal reports the determined first AI network side model corresponding to each group of second channel information to the network side device, so that the network side device can use the decoded AI network model (i.e., the second AI network model) to restore the channel characteristic information output by the corresponding encoded AI network side model (i.e., the first AI network model).
  • the decoded AI network model i.e., the second AI network model
  • the number of CSI-RS transmission ports in the CSI-RS transmission port group corresponding to the target second channel information matches the input dimension of the first AI network side model corresponding to the target second channel information, and the length of the target second channel information may match the input dimension of the first AI network side model. At this time, if the lengths of the two groups of second channel information are the same, the two groups of second channel information may correspond to the same first AI network side model.
  • the first channel information is channel information of the same layer as a target downlink channel, and the rank of the target downlink channel is greater than or equal to 1.
  • the channel information of each layer is processed independently, but the second channel information of different layers can use the same first AI network side model.
  • the grouping information of the CSI-RS transmitting ports corresponding to different layers may be the same.
  • the CSI-RS transmitting ports corresponding to layer1 are divided into 3 groups, and the CSI-RS transmitting ports corresponding to layer2 are also divided into 3 groups.
  • a group of CSI-RS transmitting ports of layer1 and a group of CSI-RS transmitting ports of layer2 may correspond to the same first AI network side model.
  • the three groups of CSI-RS transmitting ports of layer1 and the three groups of CSI-RS transmitting ports of layer2 may correspond to the same first AI network side model.
  • the phase and/or amplitude relationship between different groups of CSI-RS transmission ports in each layer can be calculated separately, or all layers of the target downlink channel can be taken as a whole to calculate the phase and/or amplitude relationship between the CSI-RS transmission ports of all layers.
  • the CSI-RS transmitting ports are grouped, and the channel information of each group of CSI-RS transmitting ports is processed using a corresponding AI network model, wherein the first channel information of a channel can be divided into K groups, and each AI network model only inputs the channel information of a corresponding group of CSI-RS transmitting ports, without having to input the channel information of all CSI-RS transmitting ports, so that an AI network model with a low number of CSI-RS transmitting ports can be used to process the channel information of a high number of CSI-RS transmitting ports, thereby improving the multiplexing efficiency and flexibility of the AI network model.
  • the information processing method provided in the embodiment of the present application may be executed by a network-side device. As shown in FIG3 , the information processing method may include the following steps:
  • Step 301 The network side device receives second information from the terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of second channel information based on a first AI network model corresponding to each of the M groups of second channel information.
  • the second information has the same meaning as the second information in the method embodiment shown in FIG. 2 , and will not be described in detail here.
  • Step 302 The network side device determines, based on the first information, a second AI network side model corresponding to each of the M channel characteristic information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, and the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports.
  • Each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and the K groups of second channel information include the M groups of second channel information, and K and M are integers greater than or equal to 1.
  • the above-mentioned first information has the same meaning as the first information in the method embodiment shown in Figure 2, and the network side device is used to determine the second AI network side model corresponding to each of the M channel characteristic information according to the first information, wherein the first AI network model for obtaining the channel characteristic information and the second AI network side model corresponding to the channel characteristic information are mutually matched AI network models or AI network models obtained by joint training, such as: the first AI network model is an encoding AI network model or the encoding part of an AI network model, and the second AI network model is a decoding AI network model or the decoding part of an AI network model.
  • Step 303 The network side device performs a second processing on the M channel characteristic information based on the second AI network side model corresponding to each of the M channel characteristic information to obtain the M groups of second channel information.
  • the second processing may include at least one of decoding, decompression, and dequantization.
  • the first information includes at least one of the following:
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports is a group of CSI-RS transmission ports.
  • the first information satisfies at least one of the following:
  • the CSI-RS transmission ports in the same group meet at least one of the following conditions:
  • the polarization direction is the same;
  • the corresponding transmitting antennas are located in the same row;
  • the corresponding transmitting antennas are located in the same column;
  • the time domain resources are the same;
  • the frequency domain resources are the same;
  • the code division multiplexing CDM method is the same;
  • the N CSI-RS transmission ports that include the first channel information are consecutively arranged, where N is an integer greater than or equal to 1, and the first channel information is the channel information of all CSI-RS transmission ports of the channel corresponding to the M groups of second channel information.
  • the first channel information includes at least one of the following:
  • the precoding matrix or vector after preprocessing is the precoding matrix or vector after preprocessing.
  • the channel matrix corresponding to the channel information of the layer has only one column, and in this case, the second channel information can be called a vector.
  • the second channel information includes channel information of at least two layers
  • the channel information of the at least two layers can also be processed into a vector by preprocessing, which is not specifically described here.
  • a set of second channel information includes at least one of the following:
  • a matrix consisting of elements in a first precoding matrix corresponding to the group of CSI-RS transmission ports, wherein the first precoding matrix is a precoding matrix for the first channel information;
  • a second precoding matrix where the second precoding matrix is a precoding matrix for the set of second channel information.
  • the information processing method further includes:
  • the network side device determines the first channel information or a precoding matrix corresponding to the first channel information according to the M groups of second channel information.
  • the network side device may splice the M groups of second channel information to obtain the first precoding matrix.
  • the information processing method before the network side device determines, according to the first information, the second AI network side model corresponding to each of the M channel feature information, the information processing method further includes:
  • the network side device receives first indication information from the terminal, where the first indication information indicates the first information.
  • the information processing method before the network side device receives the second information from the terminal, the information processing method further includes:
  • the network side device sends third information to the terminal
  • the third information indicates or configures the first information, or the third information indicates a first identifier, the first identifier is associated with the first information, and the terminal learns a first association relationship between the first information and the first identifier.
  • the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of third channel information based on the first AI network model corresponding to each of the M groups of third channel information
  • the M groups of third channel information are channel information obtained after target normalization processing on the M groups of second channel information.
  • the target normalization process includes at least one of the following:
  • the network side device performs a second processing on the M channel feature information based on a second AI network side model corresponding to each of the M channel feature information to obtain the M groups of second channel information, including:
  • the network side device receives fourth information from the terminal, where the fourth information includes a phase and/or amplitude relationship between M groups of CSI-RS transmission ports corresponding to the M groups of second channel information;
  • the network side device performs a second processing on the M channel characteristic information based on the second AI network side models corresponding to each of the M channel characteristic information to obtain the M groups of third channel information;
  • the network side device processes the M groups of third channel information into the M groups of second channel information according to the fourth information.
  • the network side device processes the M groups of third channel information into the M groups of second channel information according to the fourth information.
  • the network side device may perform inverse processing of target normalization processing on the M groups of third channel information according to the fourth information to restore the M groups of second channel information before the target normalization processing.
  • the phase and/or amplitude relationship between the M groups of CSI-RS transmission ports includes:
  • the amplitude phase difference between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports, the first group of CSI-RS transmission ports is the group where the strongest CSI-RS transmission port among the M groups of CSI-RS transmission ports is located or the strongest CSI-RS transmission port group among the M groups of CSI-RS transmission ports, and the second group of CSI-RS transmission ports is each group of CSI-RS transmission ports among the M groups of CSI-RS transmission ports except the first group of CSI-RS transmission ports.
  • the information processing method further includes:
  • the network side device receives an identifier of the first group of CSI-RS sending ports from the terminal.
  • the phase and/or amplitude relationship includes at least one of the following:
  • a phase difference between a first CSI-RS transmit port and a second CSI-RS transmit port the first CSI-RS transmit port being the strongest CSI-RS transmit port in the first group of CSI-RS transmit ports, and the second CSI-RS transmit port comprising the strongest CSI-RS transmit port in the second group of CSI-RS transmit ports;
  • a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports is a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports.
  • the correspondence between the second channel information and the first AI network side model satisfies at least one of the following:
  • the M groups of second channel information correspond to the same first AI network side model
  • the network side device indicates the first AI network side model corresponding to each of the M groups of second channel information
  • the number of CSI-RS transmitting ports in the CSI-RS transmitting port group corresponding to the target second channel information matches the input dimension of the first AI network side model corresponding to the target second channel information, and the M groups of second channel information include the target second channel information.
  • the information processing method further includes:
  • the network side device receives third indication information from the terminal, where the third indication information indicates a first AI network model corresponding to each of the M groups of second channel information.
  • the terminal selects and reports the first AI network model corresponding to each of the M groups of second channel information.
  • the network side device can determine the second AI network model corresponding to each of the M groups of second channel information according to the first AI network model corresponding to each of the M groups of second channel information, wherein the first AI network model and the second AI network model corresponding to the same group of second channel information are encoding and decoding AI network models that are matched with each other or obtained by joint training.
  • the first channel information is channel information of a same layer of a target downlink channel, and a rank of the target downlink channel is greater than or equal to 1.
  • a network side device receives M channel characteristic information from a terminal, and determines, based on the first information, a second AI network model corresponding to each of the M channel characteristic information, thereby using the second AI network model to restore the corresponding channel characteristic information into second channel information, thereby realizing the channel characteristic information reception and recovery process, wherein the input of the second AI network model is the channel characteristic information of some CSI-RS transmission ports, so that the model size of the second AI network model is small, and in addition, by dividing the same number of CSI-RS transmission ports into a group, the same second AI network model can be reused for channels with different numbers of CSI-RS transmission ports, so that the AI network model with a low number of CSI-RS transmission ports can be used to process channel information with a high number of CSI-RS transmission ports, thereby improving the multiplexing efficiency and flexibility of the AI network model.
  • the base station antenna is configured as 2 ⁇ 8, that is, 8 antennas in the horizontal direction and 2 antennas in the vertical direction, forming a rectangular antenna array, wherein each antenna is a dual-polarized antenna, forming 32 CSI-RS transmission ports, and the terminal has 4 receiving antennas.
  • the application scenario is taken as an example to illustrate the information transmission method and the information processing method provided in the embodiment of the present application:
  • the terminal can obtain a 4 ⁇ 32 channel matrix through channel estimation, divide it into two 4 ⁇ 16 channel matrices, and calculate the corresponding precoding matrix for each polarization direction to obtain two 16 ⁇ 1 precoding matrices.
  • the precoding matrix is input into the AI model.
  • the specific AI processing method is not limited. It can be preprocessed. For example, before inputting the data into the model, the data can be calculated and the 16 ⁇ 1 matrix can be preprocessed into an 8 ⁇ 1 matrix.
  • Each subband can be independent, such as grouping 13 subbands separately, or all subbands can be grouped together or some subbands can be grouped together, such as grouping 13 subbands together.
  • H [H 1 H 2 ], where H represents a 4 ⁇ 32 channel matrix, H 1 and H 2 correspond to two polarization channel matrices, respectively.
  • the terminal calculates the amplitude and phase difference between the two groups of CSI-RS transmission ports:
  • Method 1 Calculate the amplitude difference and phase difference between the coefficient with the largest amplitude in W1 and the coefficient with the largest amplitude in W2 , and quantize the calculated amplitude difference and phase difference and report them to the base station.
  • Method 2 Calculate the equivalent channel matrix H 1 W 1 corresponding to W 1 and the equivalent channel matrix H 2 W 2 corresponding to W 2 , calculate the power ratio of the two equivalent channels and report it to the base station.
  • the command type (cmd-Type) is indicated as: cmd8-FD2-TD4, that is, there are 8 CDM modes in the CDM group (group), with 2 frequency domain positions and 4 time domain positions, then the CSI-RS transmission ports can be grouped as follows:
  • Method 1 Divide into 8 groups, one group for each CDM, and 4 ports in each group. Input the second channel information of each group of CSI-RS transmission ports into the corresponding AI model to obtain 8 channel feature information;
  • Method 2 Divide into 4 groups, one group for each time-frequency position, and 8 ports in each group. Input the second channel information of each group of CSI-RS sending ports into the corresponding AI model to obtain 4 groups of channel feature information.
  • the information transmission method provided in the embodiment of the present application can be executed by an information transmission device.
  • the information transmission device provided in the embodiment of the present application is described by taking the information transmission method executed by the information transmission device as an example.
  • An information transmission device provided in an embodiment of the present application may be a device in a terminal. As shown in FIG4 , the information transmission device 400 may include the following modules:
  • a first determining module 401 is configured to determine K groups of second channel information from the first channel information based on the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond to the K groups of CSI-RS transmission ports in one-to-one correspondence, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1;
  • a first processing module 402 is used to perform a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel characteristic information, and the K groups of second channel information include the M groups of second channel information;
  • the first sending module 403 is used to send second information to the network side device, where the second information includes the M channel characteristic information.
  • the first information includes at least one of the following:
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports is a group of CSI-RS transmission ports.
  • the first information satisfies at least one of the following:
  • the CSI-RS transmission ports in the same group satisfy at least one of the following:
  • the polarization direction is the same;
  • the corresponding transmitting antennas are located in the same row;
  • the corresponding transmitting antennas are located in the same column;
  • the time domain resources are the same;
  • the frequency domain resources are the same;
  • the code division multiplexing CDM method is the same;
  • N The N CSI-RS transmission ports that are consecutively arranged among all the CSI-RS transmission ports that include the first channel information, where N is an integer greater than or equal to 1.
  • the first channel information includes at least one of the following:
  • the precoding matrix or vector after preprocessing is the precoding matrix or vector after preprocessing.
  • a set of second channel information includes at least one of the following:
  • a matrix consisting of elements in a first precoding matrix corresponding to the group of CSI-RS transmission ports, wherein the first precoding matrix is a precoding matrix for the first channel information;
  • a second precoding matrix where the second precoding matrix is a precoding matrix for the set of second channel information.
  • the information transmission device 400 further includes:
  • a third determination module configured to determine the first information according to the first AI network side model
  • the second sending module is used to send first indication information to the network side device, where the first indication information indicates the first information.
  • the information transmission device 400 further includes:
  • a second receiving module used to receive third information from the network side device
  • the third information indicates or configures the first information, or the third information indicates a first identifier, the first identifier is associated with the first information, and the terminal learns a first association relationship between the first information and the first identifier.
  • the first processing module 402 includes:
  • a first processing unit configured to perform target normalization processing on the M groups of second channel information respectively to obtain M groups of third channel information
  • the second processing unit is used to perform a first processing on the M groups of third channel information based on the first AI network models corresponding to each of the M groups of third channel information to obtain M channel feature information.
  • the target normalization process includes at least one of the following:
  • the information transmission device 400 further includes:
  • the third sending module is used to send fourth information to the network side device, where the fourth information includes the phase and/or amplitude relationship between the M groups of CSI-RS sending ports corresponding to the M groups of second channel information.
  • phase and/or amplitude relationship between the M groups of CSI-RS transmission ports includes:
  • the amplitude and/or phase difference between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports, the first group of CSI-RS transmission ports is the group where the strongest CSI-RS transmission port among the M groups of CSI-RS transmission ports is located or the strongest CSI-RS transmission port group among the M groups of CSI-RS transmission ports, and the second group of CSI-RS transmission ports is each group of CSI-RS transmission ports among the M groups of CSI-RS transmission ports except the first group of CSI-RS transmission ports.
  • the information transmission device 400 further includes:
  • the fourth sending module is used to send the identifier of the first group of CSI-RS sending ports to the network side device.
  • phase and/or amplitude relationship includes at least one of the following:
  • a phase difference between a first CSI-RS transmit port and a second CSI-RS transmit port the first CSI-RS transmit port being the strongest CSI-RS transmit port in the first group of CSI-RS transmit ports, and the second CSI-RS transmit port comprising the strongest CSI-RS transmit port in the second group of CSI-RS transmit ports;
  • a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports is a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports.
  • the correspondence between the second channel information and the first AI network side model satisfies at least one of the following:
  • the M groups of second channel information correspond to the same first AI network side model
  • the network side device indicates the first AI network side model corresponding to each of the M groups of second channel information
  • the number of CSI-RS transmitting ports in the CSI-RS transmitting port group corresponding to the target second channel information matches the input dimension of the first AI network side model corresponding to the target second channel information, and the M groups of second channel information include the target second channel information.
  • the information transmission device 400 further includes:
  • the fifth sending module is used to send third indication information to the network side device, where the third indication information indicates the first AI network model corresponding to each of the M groups of second channel information.
  • the first channel information is channel information of the same layer as the target downlink channel.
  • the rank of is greater than or equal to 1.
  • the information transmission device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.
  • the electronic device can be a terminal, or it can be other devices other than a terminal.
  • the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.
  • the information transmission device 400 provided in the embodiment of the present application can implement each process implemented by the terminal in the method embodiment shown in Figure 2, and can achieve the same beneficial effects. To avoid repetition, it will not be described here.
  • the information processing method provided in the embodiment of the present application can be executed by an information processing device.
  • the information processing device provided in the embodiment of the present application is described by taking the information processing device executing the information processing method as an example.
  • An information processing device provided in an embodiment of the present application may be a device in a network-side device. As shown in FIG5 , the information processing device 500 may include the following modules:
  • a first receiving module 501 is used to receive second information from a terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information;
  • a second determination module 502 is used to determine, according to the first information, a second AI network side model corresponding to each of the M channel characteristic information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, the K groups of second channel information include the M groups of second channel information, and K and M are integers greater than or equal to 1;
  • the second processing module 503 is used to perform a second processing on the M channel characteristic information based on the second AI network side model corresponding to each of the M channel characteristic information to obtain the M groups of second channel information.
  • the first information includes at least one of the following:
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports is a group of CSI-RS transmission ports.
  • the first information satisfies at least one of the following:
  • the CSI-RS transmission ports in the same group satisfy at least one of the following:
  • the polarization direction is the same;
  • the corresponding transmitting antennas are located in the same row;
  • the corresponding transmitting antennas are located in the same column;
  • the time domain resources are the same;
  • the frequency domain resources are the same;
  • the code division multiplexing CDM method is the same;
  • the N CSI-RS transmission ports that include the first channel information are consecutively arranged, where N is an integer greater than or equal to 1, and the first channel information is the channel information of all CSI-RS transmission ports of the channel corresponding to the M groups of second channel information.
  • the first channel information includes at least one of the following:
  • the precoding matrix or vector after preprocessing is the precoding matrix or vector after preprocessing.
  • a set of second channel information includes at least one of the following:
  • a matrix consisting of elements in a first precoding matrix corresponding to the group of CSI-RS transmission ports, wherein the first precoding matrix is a precoding matrix for the first channel information;
  • a second precoding matrix where the second precoding matrix is a precoding matrix for the set of second channel information.
  • the information processing device 500 further includes:
  • a fourth determination module is used to determine the first channel information or a precoding matrix corresponding to the first channel information according to the M groups of second channel information.
  • the information processing device 500 further includes:
  • the third receiving module is used to receive first indication information from the terminal, where the first indication information indicates the first information.
  • the information processing device 500 further includes:
  • a sixth sending module configured to send third information to the terminal
  • the third information indicates or configures the first information, or the third information indicates a first identifier, the first identifier is associated with the first information, and the terminal learns a first association relationship between the first information and the first identifier.
  • the M channel characteristic information is channel characteristic information obtained by performing a first processing on the M groups of third channel information based on the first AI network model corresponding to each of the M groups of third channel information
  • the M groups of third channel information are channel information obtained after target normalization processing on the M groups of second channel information.
  • the target normalization process includes at least one of the following:
  • the second processing module 503 includes:
  • a receiving unit configured to receive fourth information from the terminal, wherein the fourth information includes M groups of second channel information. phase and/or amplitude relationship between M groups of CSI-RS transmission ports corresponding to the information;
  • a third processing unit configured to perform a second processing on the M channel characteristic information based on the second AI network side models corresponding to the respective M channel characteristic information to obtain the M groups of third channel information;
  • the fourth processing unit is configured to process the M groups of third channel information into the M groups of second channel information according to the fourth information.
  • phase and/or amplitude relationship between the M groups of CSI-RS transmission ports includes:
  • the first group of CSI-RS transmission ports is the group where the strongest CSI-RS transmission port among the M groups of CSI-RS transmission ports is located or the strongest CSI-RS transmission port group among the M groups of CSI-RS transmission ports
  • the second group of CSI-RS transmission ports is each group of CSI-RS transmission ports among the M groups of CSI-RS transmission ports except the first group of CSI-RS transmission ports.
  • the information processing device 500 further includes:
  • the fourth receiving module is configured to receive an identifier of the first group of CSI-RS sending ports from the terminal.
  • phase and/or amplitude relationship includes at least one of the following:
  • a phase difference between a first CSI-RS transmit port and a second CSI-RS transmit port the first CSI-RS transmit port being the strongest CSI-RS transmit port in the first group of CSI-RS transmit ports, and the second CSI-RS transmit port comprising the strongest CSI-RS transmit port in the second group of CSI-RS transmit ports;
  • a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports is a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports.
  • the correspondence between the second channel information and the first AI network side model satisfies at least one of the following:
  • the M groups of second channel information correspond to the same first AI network side model
  • the network side device indicates the first AI network side model corresponding to each of the M groups of second channel information
  • the number of CSI-RS transmitting ports in the CSI-RS transmitting port group corresponding to the target second channel information matches the input dimension of the first AI network side model corresponding to the target second channel information, and the M groups of second channel information include the target second channel information.
  • the information processing device 500 further includes:
  • a fifth receiving module is used to receive third indication information from the terminal, where the third indication information indicates the first AI network model corresponding to each of the M groups of second channel information.
  • the first channel information is channel information of a same layer of a target downlink channel, and a rank of the target downlink channel is greater than or equal to 1.
  • the information processing device 500 provided in the embodiment of the present application can implement each process implemented by the network side device in the method embodiment shown in Figure 3, and can achieve the same beneficial effects. To avoid repetition, it will not be described here.
  • the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, wherein the memory 602 stores a program or instruction that can be run on the processor 601, for example, the communication
  • the device 600 is a terminal
  • the program or instruction is executed by the processor 601 to implement the various steps of the method embodiment shown in Figure 2, and can achieve the same technical effect.
  • the communication device 600 is a network side device
  • the program or instruction is executed by the processor 601 to implement the various steps of the method embodiment shown in Figure 3, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a terminal, including a processor and a communication interface, wherein the processor is used to determine K groups of second channel information from the first channel information based on first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS sending ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS sending ports, each group of CSI-RS sending ports in the K groups of CSI-RS sending ports includes at least one CSI-RS sending port, and K is an integer greater than or equal to 1; the processor is also used to perform a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel characteristic information, the K groups of second channel information include the M groups of second channel information; the communication interface is used to send second information to a network side device, and the second information includes the M channel characteristic information.
  • the processor is used to determine K groups of second channel information from the first channel information based on first information,
  • FIG7 is a schematic diagram of the hardware structure of a terminal implementing the embodiment of the present application.
  • the terminal 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709 and at least some of the components of a processor 710.
  • the terminal 700 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 710 through a power management system, so as to implement functions such as managing charging, discharging, and power consumption management through the power management system.
  • a power source such as a battery
  • the terminal structure shown in FIG7 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.
  • the input unit 704 may include a graphics processing unit (GPU) 7041 and a microphone 7042, and the graphics processor 7041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode.
  • the display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc.
  • the user input unit 707 includes a touch panel 7071 and at least one of other input devices 7072.
  • the touch panel 7071 is also called a touch screen.
  • the touch panel 7071 may include two parts: a touch detection device and a touch controller.
  • Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.
  • the RF unit 701 can transmit the data to the processor 710 for processing; in addition, the RF unit 701 can send uplink data to the network side device.
  • the RF unit 701 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.
  • the memory 709 can be used to store software programs or instructions and various data.
  • the memory 709 can mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area can store an operating system,
  • the memory 709 may include an application program or instruction required for one less function (such as a sound playback function, an image playback function, etc.).
  • the memory 709 may include a volatile memory or a non-volatile memory, or the memory 709 may include both volatile and non-volatile memories.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM).
  • the memory 709 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.
  • the processor 710 may include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 710.
  • the processor 710 is configured to determine K groups of second channel information from the first channel information based on the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond to the K groups of CSI-RS transmission ports one by one, each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port, and K is an integer greater than or equal to 1;
  • the processor 710 is further configured to perform a first process on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel feature information, wherein the K groups of second channel information include the M groups of second channel information;
  • the radio frequency unit 701 is used to send second information to the network side device, where the second information includes the M channel characteristic information.
  • the first information includes at least one of the following:
  • the identifier of the CSI-RS transmission port in each group of CSI-RS transmission ports is a group of CSI-RS transmission ports.
  • the first information satisfies at least one of the following:
  • the CSI-RS transmission ports in the same group satisfy at least one of the following:
  • the polarization direction is the same;
  • the corresponding transmitting antennas are located in the same row;
  • the corresponding transmitting antennas are located in the same column;
  • the time domain resources are the same;
  • the frequency domain resources are the same;
  • the code division multiplexing CDM method is the same;
  • N The N CSI-RS transmission ports that are consecutively arranged among all the CSI-RS transmission ports that include the first channel information, where N is an integer greater than or equal to 1.
  • the first channel information includes at least one of the following:
  • the precoding matrix or vector after preprocessing is the precoding matrix or vector after preprocessing.
  • a set of second channel information includes at least one of the following:
  • a matrix consisting of elements in a first precoding matrix corresponding to the group of CSI-RS transmission ports, wherein the first precoding matrix is a precoding matrix for the first channel information;
  • a second precoding matrix where the second precoding matrix is a precoding matrix for the set of second channel information.
  • the processor 710 is further configured to determine the first information according to the first AI network side model
  • the radio frequency unit 701 is further used to send first indication information to the network side device, where the first indication information indicates the first information.
  • the radio frequency unit 701 is further configured to receive third information from the network side device;
  • the third information indicates or configures the first information, or the third information indicates a first identifier, the first identifier is associated with the first information, and the terminal learns a first association relationship between the first information and the first identifier.
  • the processor 710 performs a first processing on the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information to obtain M channel feature information, including:
  • the M groups of third channel information are first processed to obtain M channel feature information.
  • the target normalization process includes at least one of the following:
  • the radio frequency unit 701 is further configured to send fourth information to the network side device, wherein the fourth information includes: The phase and/or amplitude relationship between the M groups of CSI-RS transmission ports corresponding to the M groups of second channel information.
  • phase and/or amplitude relationship between the M groups of CSI-RS transmission ports includes:
  • the amplitude and/or phase difference between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports, the first group of CSI-RS transmission ports is the group where the strongest CSI-RS transmission port among the M groups of CSI-RS transmission ports is located or the strongest CSI-RS transmission port group among the M groups of CSI-RS transmission ports, and the second group of CSI-RS transmission ports is each group of CSI-RS transmission ports among the M groups of CSI-RS transmission ports except the first group of CSI-RS transmission ports.
  • the radio frequency unit 701 is further configured to send an identifier of the first group of CSI-RS sending ports to the network side device.
  • phase and/or amplitude relationship includes at least one of the following:
  • a phase difference between a first CSI-RS transmit port and a second CSI-RS transmit port the first CSI-RS transmit port being the strongest CSI-RS transmit port in the first group of CSI-RS transmit ports, and the second CSI-RS transmit port comprising the strongest CSI-RS transmit port in the second group of CSI-RS transmit ports;
  • a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports is a power adjustment factor between the first group of CSI-RS transmission ports and the second group of CSI-RS transmission ports.
  • the correspondence between the second channel information and the first AI network side model satisfies at least one of the following:
  • the M groups of second channel information correspond to the same first AI network side model
  • the network side device indicates the first AI network side model corresponding to each of the M groups of second channel information
  • the number of CSI-RS transmitting ports in the CSI-RS transmitting port group corresponding to the target second channel information matches the input dimension of the first AI network side model corresponding to the target second channel information, and the M groups of second channel information include the target second channel information.
  • the radio frequency unit 701 is also used to send third indication information to the network side device, where the third indication information indicates the first AI network model corresponding to each of the M groups of second channel information.
  • the first channel information is channel information of a same layer of a target downlink channel, and a rank of the target downlink channel is greater than or equal to 1.
  • the terminal 700 provided in the embodiment of the present application can implement each process performed by the information transmission device shown in Figure 4, and can achieve the same beneficial effects. To avoid repetition, it will not be described here.
  • An embodiment of the present application also provides a network side device, including a processor and a communication interface, wherein the communication interface is used to receive second information from a terminal, wherein the second information includes M channel characteristic information, and the M channel characteristic information is channel characteristic information obtained by first processing the M groups of second channel information based on the first AI network model corresponding to each of the M groups of second channel information; the processor is used to determine the second AI network side model corresponding to each of the M channel characteristic information according to the first information, wherein the first information includes grouping information of K groups of channel state information reference signal CSI-RS transmission ports, the K groups of second channel information correspond one-to-one to the K groups of CSI-RS transmission ports, and each group of CSI-RS transmission ports in the K groups of CSI-RS transmission ports includes at least one CSI-RS transmission port
  • the sending port, the K groups of second channel information include the M groups of second channel information, K and M are integers greater than or equal to 1; the processor is also used to perform a second processing on the M channel feature information
  • the network side device 800 includes: an antenna 801, a radio frequency device 802, a baseband device 803, a processor 804 and a memory 805.
  • the antenna 801 is connected to the radio frequency device 802.
  • the radio frequency device 802 receives information through the antenna 801 and sends the received information to the baseband device 803 for processing.
  • the baseband device 803 processes the information to be sent and sends it to the radio frequency device 802.
  • the radio frequency device 802 processes the received information and sends it out through the antenna 801.
  • the method executed by the network-side device in the above embodiment may be implemented in the baseband device 803, which includes a baseband processor.
  • the baseband device 803 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 8, one of which is, for example, a baseband processor, which is connected to the memory 805 through a bus interface to call the program in the memory 805 and execute the network device operations shown in the above method embodiment.
  • the network side device may also include a network interface 806, which is, for example, a Common Public Radio Interface (CPRI).
  • CPRI Common Public Radio Interface
  • the network side device 800 of the embodiment of the present application also includes: instructions or programs stored in the memory 805 and executable on the processor 804.
  • the processor 804 calls the instructions or programs in the memory 805 to execute the methods executed by the modules shown in Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored.
  • a program or instruction is stored.
  • the various processes of the method embodiment shown in Figure 2 or Figure 3 are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.
  • the processor is the processor in the terminal described in the above embodiment.
  • the readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.
  • An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the method embodiment shown in Figure 2 or Figure 3, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
  • the embodiments of the present application further provide a computer program/program product, which is stored in a storage medium, and is executed by at least one processor to implement the various processes of the method embodiment shown in Figure 2 or Figure 3, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.
  • the present application also provides a communication system, including: a terminal and a network side device, wherein the terminal can be used to perform
  • the network side device can be used to execute the steps of the information transmission method shown in Figure 2, and the network side device can be used to execute the steps of the information processing method shown in Figure 3.
  • the technical solution of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.
  • a storage medium such as ROM/RAM, a magnetic disk, or an optical disk
  • a terminal which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

本申请公开了一种信息传输方法、信息处理方法、装置和通信设备，属于通信技术领域，本申请实施例的信息传输方法包括：终端基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；所述终端向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。

Description

信息传输方法、信息处理方法、装置和通信设备
相关申请的交叉引用
本申请主张在2022年10月27日在中国提交的中国专利申请No.202211329924.7的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于通信技术领域，具体涉及一种信息传输方法、信息处理方法、装置和通信设备。
背景技术
在相关技术中，对借助人工智能(Artificial Intelligence，AI)网络模型来传输信道特征信息的方法进行了研究。
该AI网络模型可以包括编码部分(即编码AI网络模型)和解码部分(即解码AI网络模型)，编码AI网络模型用于将信道信息编码成信道特征信息，解码AI网络模型用于将编码AI网络模型输出的信道特征信息恢复成信道信息。
相关技术中，同一个AI网络模型的输入维度是固定的，对于不同的信道状态信息—参考信号(Channel State Information-Reference Signal，CSI-RS)端口数的信道信息，需要使用不同的AI网络模型，例如：因为8个CSI-RS端口训练的AI网络模型无法在16个CSI-RS端口的信道下使用，从而需要训练以及传递与各个CSI-RS端口数匹配的AI网络模型，这样会增加训练与各个CSI-RS端口数匹配的AI网络模型的计算量，且增加传输与各个CSI-RS端口数匹配的AI网络模型的开销。
发明内容
本申请实施例提供一种信息传输方法、信息处理方法、装置和通信设备，使得低CSI-RS端口数的AI网络模型可以处理高CSI-RS端口数的信道信息，从而提高了AI网络模型的复用效率和灵活度。
第一方面，提供了一种信息传输方法，该方法包括：
终端基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道 信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
所述终端向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
第二方面，提供了一种信息传输装置，应用于终端，该装置包括：
第一确定模块，用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
第一处理模块，用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
第一发送模块，用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
第三方面，提供了一种信息处理方法，包括：
网络侧设备接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；
所述网络侧设备根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；
所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
第四方面，提供了一种信息处理装置，应用于网络侧设备，该装置包括：
第一接收模块，用于接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；
第二确定模块，用于根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；
第二处理模块，用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
第五方面，提供了一种通信设备，该通信设备包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面或第三方面所述的方法的步骤。
第六方面，提供了一种终端，包括处理器及通信接口，其中，所述处理器用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；所述处理器还用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；所述通信接口用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
第七方面，提供了一种网络侧设备，包括处理器及通信接口，其中，所述通信接口用于接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；所述处理器用于根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；所述处理器还用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
第八方面，提供了一种通信系统，包括：终端和网络侧设备，所述终端可用于执行如第一方面所述的信息传输方法的步骤，所述网络侧设备可用于执行如第三方面所述的信息处理方法的步骤。
第九方面，提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的信息传输方法的步骤，或者实现如第三方面所述的信息处理方法的步骤。
第十方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的信息传输方法，或实现如第三方面所述的信息处理方法。
第十一方面，提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如第一方面所述的信息传输方法的步骤，或者所述计算机程序/程序产品被至少一个处理器执行以实现如第三方面所述的信息处理方法的步骤。
在本申请实施例中，对CSI-RS发送端口进行分组，每一组CSI-RS发送端口的信道信 息采用对应一个AI网络模型进行处理，其中，一个信道的第一信道信息能够划分为K组，且每一个AI网络模型仅输入对应的一组CSI-RS发送端口的信道信息，而无需输入全部CSI-RS发送端口的信道信息，这样，低CSI-RS发送端口数的AI网络模型能够用于处理高CSI-RS发送端口数的信道信息，从而提高了AI网络模型的复用效率和灵活度。
附图说明
图1是本申请实施例能够应用的一种无线通信系统的结构示意图；
图2是本申请实施例提供的一种信息传输方法的流程图；
图3是本申请实施例提供的一种信息处理方法的流程图；
图4是本申请实施例提供的一种信息传输装置的结构示意图；
图5是本申请实施例提供的一种信息处理装置的结构示意图；
图6是本申请实施例提供的一种通信设备的结构示意图；
图7是本申请实施例提供的一种终端的硬件结构示意图
图8是本申请实施例提供的一种网络侧设备的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。
值得指出的是，本申请实施例所描述的技术不限于长期演进型(Long Term Evolution，LTE)/LTE的演进(LTE-Advanced，LTE-A)系统，还可用于其他无线通信系统，诸如码分多址(Code Division Multiple Access，CDMA)、时分多址(Time Division Multiple Access，TDMA)、频分多址(Frequency Division Multiple Access，FDMA)、正交频分多址(Orthogonal Frequency Division Multiple Access，OFDMA)、单载波频分多址(Single-carrier Frequency Division Multiple Access，SC-FDMA)和其他系统。本申请实施例中的术语“系统”和“网络”常被可互换地使用，所描述的技术既可用于以上提及的系统和无线电技术，也可用于其他系统和无线电技术。以下描述出于示例目的描述了新空口(New Radio，NR)系统，并且在以下大部分描述中使用NR术语，但是这些技术也可应用于NR系统应用以外的应用，如第6代(6th Generation，6G)通信系统。
图1示出本申请实施例可应用的一种无线通信系统的框图。无线通信系统包括终端11和网络侧设备12。其中，终端11可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、机器人、可穿戴式设备(Wearable Device)、车载设备(Vehicle User Equipment，VUE)、行人终端(Pedestrian User Equipment，PUE)、智能家居(具有无线通信功能的家居设备，如冰箱、电视、洗衣机或者家具等)、游戏机、个人计算机(personal computer，PC)、柜员机或者自助机等终端侧设备，可穿戴式设备包括：智能手表、智能手环、智能耳机、智能眼镜、智能首饰(智能手镯、智能手链、智能戒指、智能项链、智能脚镯、智能脚链等)、智能腕带、智能服装等。需要说明的是，在本申请实施例并不限定终端11的具体类型。网络侧设备12可以包括接入网设备或核心网设备，其中，接入网设备也可以称为无线接入网设备、无线接入网(Radio Access Network,RAN)、无线接入网功能或无线接入网单元。接入网设备可以包括基站、无线局域网(Wireless Local Area Network，WLAN)接入点或WiFi节点等，基站可被称为节点B、演进节点B(eNB)、接入点、基收发机站(Base Transceiver Station，BTS)、无线电基站、无线电收发机、基本服务集(Basic Service Set，BSS)、扩展服务集(Extended Service Set，ESS)、家用B节点、家用演进型B节点、发送接收点(Transmitting Receiving Point，TRP)或所述领域中其他某个合适的术语，只要达到相同的技术效果，所述基站不限于特定技术词汇，需要说明的是，在本申请实施例中仅以NR系统中的基站为例进行介绍，并不限定基站的具体类型。
由信息论可知，准确的信道状态信息(channel state information，CSI)对信道容量的至关重要。尤其是对于多天线系统来讲，发送端可以根据CSI优化信号的发送，使其更加匹配信道的状态。如：信道质量指示(channel quality indicator，CQI)可以用来选择合适的调制编码方案(modulation and coding scheme，MCS)实现链路自适应；预编码矩阵指示(precoding matrix indicator，PMI)可以用来实现特征波束成形(eigen beamforming)从而最大化接收信号的强度，或者用来抑制干扰(如小区间干扰、多用户之间干扰等)。因此，自从多天线技术(multi-input multi-output，MIMO)被提出以来，CSI获取一直都是研究热点。
通常，基站在某个时隙(slot)的某些时频资源上发送CSI参考信号(CSI Reference Signal，CSI-RS)，终端根据CSI-RS进行信道估计，计算这个slot上的信道信息，通过码本将PMI反馈给基站，基站根据终端反馈的码本信息组合出信道信息，在下一次CSI上报之前，基站以此进行数据预编码及多用户调度。
为了进一步减少CSI反馈开销，终端可以将每个子带上报PMI改成按照时延(delay)上报PMI，由于delay域的信道更集中，用更少的delay的PMI就可以近似表示全部子带 的PMI，即将delay域信息压缩之后再上报。
同样，为了减少开销，基站可以事先对CSI-RS进行预编码，将编码后的CSI-RS发送个终端，终端看到的是经过编码之后的CSI-RS对应的信道，终端只需要在网络侧指示的端口中选择若干个强度较大的端口，并上报这些端口对应的系数即可。
进一步，为了更好的压缩信道信息，可以使用神经网络或机器学习的方法。
人工智能目前在各个领域获得了广泛的应用。AI模块有多种实现方式，例如神经网络、决策树、支持向量机、贝叶斯分类器等。本申请以神经网络为例进行说明，但是并不限定AI模块的具体类型。
神经网络的参数通过优化算法进行优化。优化算法就是一种能够帮我们最小化或者最大化目标函数(有时候也叫损失函数)的一类算法。而目标函数往往是模型参数和数据的数学组合。例如给定数据X和其对应的标签Y，我们构建一个神经网络模型f(.)，有了模型后，根据输入x就可以得到预测输出f(x)，并且可以计算出预测值和真实值之间的差距(f(x)-Y)，这个就是损失函数。我们的目的是找到合适的权值和偏置，使上述的损失函数的值达到最小，损失值越小，则说明我们的模型越接近于真实情况。
目前常见的优化算法，基本都是基于误差反向传播(error Back Propagation，BP)算法。BP算法的基本思想是，学习过程由信号的正向传播与误差的反向传播两个过程组成。正向传播时，输入样本从输入层传入，经各隐层逐层处理后，传向输出层。若输出层的实际输出与期望的输出不符，则转入误差的反向传播阶段。误差反传是将输出误差以某种形式通过隐层向输入层逐层反传，并将误差分摊给各层的所有单元，从而获得各层单元的误差信号，此误差信号即作为修正各单元权值的依据。这种信号正向传播与误差反向传播的各层权值调整过程，是周而复始地进行的。权值不断调整的过程，也就是网络的学习训练过程。此过程一直进行到网络输出的误差减少到可接受的程度，或进行到预先设定的学习次数为止。
常见的优化算法有梯度下降(Gradient Descent)、随机梯度下降(Stochastic Gradient Descent，SGD)、小批量梯度下降(mini-batch gradient descent)、动量法(Momentum)、带动量的随机梯度下降(Nesterov)、自适应梯度下降(Adaptive gradient descent，Adagrad)、自适应学习率调整(Adadelta)、均方根误差降速(root mean square prop，RMSprop)、自适应动量估计(Adaptive Moment Estimation，Adam)等。
这些优化算法在误差反向传播时，都是根据损失函数得到的误差/损失，对当前神经元求导数/偏导，加上学习速率、之前的梯度/导数/偏导等影响，得到梯度，将梯度传给上一层。
CSI压缩恢复流程为：终端估计CSI-RS，计算信道信息，将计算的信道信息或者原始的估计到的信道信息通过编码AI网络模型得到编码结果，将编码结果发送给基站，基站接收编码后的结果，输入到解码AI网络模型中，恢复信道信息。
具体的，基于神经网络的CSI压缩反馈方案是，在终端对信道信息进行压缩编码，将 压缩后的内容发送给基站，在基站对压缩后的内容进行解码，从而恢复信道信息，此时基站的解码AI网络模型和终端的编码AI网络模型需要联合训练，达到合理的匹配度。编码AI网络模型的输入是信道信息，输出是编码信息，即信道特征信息，解码AI网络模型的输入是编码信息，输出是恢复的信道信息。
通常，输入编码AI网络模型的信道信息是每个子带的信道矩阵或预编码矩阵，以预编码矩阵为例，预编码矩阵的列数为秩(rank)数，即层(layer)总数，预编码矩阵的行数为CSI-RS端口数，这样，编码AI网络模型的输入维度是由rank数，CSI-RS端口数，以及子带数共同决定的。在相关技术中，每一个信道的信道信息采用一个编码AI网络模型进行处理，当该信道的CSI-RS端口数发生变化的情况下，该信道的信道信息与CSI-RS端口数变化前使用的编码AI网络模型不再匹配，此时，需要重新训练新的编码AI网络模型和解码AI网络模型，这样，为了适用多种的天线端口数量和天线端口结构，需要提前训练很多个AI网络模型，增加AI网络模型的训练复杂度，同时也提高了传输AI网络模型的开销，提高了编码AI网络模型与解码AI网络模型匹配的复杂度和匹配时间。
本申请实施例中，对CSI-RS发送端口进行分组，位于一个组内的至少一个CSI-RS发送端口对应的信道信息采用一个AI网络模型进行处理，使得低CSI-RS发送端口数的AI网络模型能够处理高CSI-RS发送端口数的信道信息，且降低了AI网络模型的数量，以及降低了AI网络模型的大小。
例如：一个信道配置32个CSI-RS发送端口，将其划分为4组，每一组8个CSI-RS发送端口，则一个AI网络模型仅需处理8个CSI-RS发送端口的信道信息。当该信道的CSI-RS发送端口数变更为16个时，将其划分为2组，每一组还是8个CSI-RS发送端口，此时，可以复用同一个AI网络模型来处理该2组CSI-RS发送端口的信道信息。
下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的信息传输方法、信息处理方法、信息传输装置、信息处理装置及通信设备等进行详细地说明。
请参阅图2，本申请实施例提供的一种信息传输方法，其执行主体是终端，如图2所示，该终端执行的信息传输方法可以包括以下步骤：
步骤201、终端基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数。
其中，第一信道信息可以是某一信道的原始的信道矩阵或预编码矩阵，或者是预处理后的信道矩阵或预编码矩阵，为了便于说明，本申请实施例中通常以信道信息为预编码矩阵为例进行举例说明，在此不构成具体限定。
可选地，一组第二信道信息包括以下至少一项：
一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
其中，第一预编码矩阵可以是以所述第一信道信息为一个整体确定的预编码矩阵，第二预编码矩阵可以是以一组第二信道信息为整体确定的预编码矩阵。
一种实施方式中，K组第二信道信息中任意两组第二信道信息可以部分重叠，例如：假设目标信道包括32个CSI-RS发送端口，K等于3，则第一组第二信道信息包括第0至11个CSI-RS发送端口，第二组第二信道信息包括第12至21个CSI-RS发送端口，第三组第二信道信息包括第20至31个CSI-RS发送端口。
一种实施方式中，K组第二信道信息中任意两组第二信道信息不重叠。
一种实施方式中，K组第二信道信息的长度相同。
一种实施方式中，K组第二信道信息的长度可以不相同，例如：假设K等于2，一组第二信道信息包括4个CSI-RS发送端口的信道信息，另一种包括8个CSI-RS发送端口的信道信息。
步骤202、所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息。
其中，第一AI网络模型可以是编码AI网络模型，所述第一处理可以包括：编码、压缩、量化等处理中的至少一项。
一种实施方式中，M组第二信道信息可以对应相同的第一AI网络模型，此时，M组第二信道信息的长度相同或者不同，其中，对于长度不同的第二信道信息，可以采用补零的方式调整为长度一致的信道信息，并对调整后的，长度一致的信道信息采用相同的第一AI网络模型进行处理。
一种实施方式中，M组第二信道信息各自对应的第一AI网络模型互不相同。
一种实施方式中，M组第二信道信息中的一部分对应相同的第一AI网络模型，M组第二信道信息中的另一部分对应互不相同的第一AI网络模型。
步骤203、所述终端向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
一种实施方式中，终端通过CSI报告上报M个信道特征信息。
在实施中，网络侧设备在接收到M个信道特征信息的情况下，基于M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，以恢复所述M组第二信道信息。其中，第二处理可以是解码、解压缩、解量化等处理中的至少一项。
作为一种可选的实施方式，所述第一信息包括以下至少一项：
K的取值；
每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
选项一，K的取值用于指示将目标信道的全部CSI-RS发送端口划分为几组。
一种实施方式中，终端可以将目标信道的全部CSI-RS发送端口均匀的划分为K组，例如：假设目标信道包括X个CSI-RS发送端口，基站指示或协议约定K的取值，则终端确定每一组CSI-RS发送端口包括X÷K个CSI-RS发送端口。
值得提出的是，在实施中，可能存在X与K不能整除的情况，此时，对于不能整除的处理方式，可以由协议约定，基站指示或由终端决定并上报。例如：假设X等于16，K等于5，则终端可以将X个CSI-RS发送端口划分为5组，每一组包括3个CSI-RS发送端口，且剩余的1个CSI-RS发送端口可以不上报。
一种实施方式中，终端可以将目标信道的全部CSI-RS发送端口划分为长度不完全相同的K组。
选项二，每一组CSI-RS发送端口内的CSI-RS发送端口的数目，可以是：第一组CSI-RS发送端口包括A1个CSI-RS发送端口，第二组CSI-RS发送端口包括A2个CSI-RS发送端口……，第K组CSI-RS发送端口包括Ak个CSI-RS发送端口。这样，终端可以据此确定K组CSI-RS发送端口中每一组包含的CSI-RS发送端口数目。
一种实施方式中，一组CSI-RS发送端口内的CSI-RS发送端口可以是连续排列的CSI-RS发送端口。
选项三，每一组CSI-RS发送端口内的CSI-RS发送端口的标识，可以是：确定每一组CSI-RS发送端口具体包含哪一个或哪一些CSI-RS发送端口，其中，CSI-RS发送端口的标识可以是数字，如CSI-RS发送端口的排列顺序。例如：第一组CSI-RS发送端口包括CSI-RS发送端口1,3,5；第二组CSI-RS发送端口包括CSI-RS发送端口2,4,6。
一种实施方式中，一组CSI-RS发送端口内的CSI-RS发送端口可以是非连续排列的CSI-RS发送端口。
作为一种可选的实施方式，所述第一信息满足以下至少一项：
由所述网络侧设备指示；
由所述终端选择；
由协议约定。
一种实施方式中，网络侧设备可以通过信令指示CSI-RS发送端口的分组信息，例如：包括分组数量K，或每组CSI-RS发送端口内的CSI-RS发送端口数目，或每组CSI-RS发送端口内具体包括哪个或哪些CSI-RS发送端口。
一种实施方式中，网络侧设备可以在CSI报告配置(report config)中配置CSI-RS发送端口的分组信息。
一种实施方式中，终端可以根据自身具有的第一AI网络模型的输入维度来确定端口的分组方式，以使分组后的每一组第二信道信息，与终端具有的第一AI网络模型的输入维度匹配。
在实施中，若终端选择或确定第一信息，则终端可以向网络侧设备上报所述第一信息。
一种实施方式中，CSI-RS发送端口的分组信息可以是协议约定的。
例如：协议约定位于同一组内的CSI-RS发送端口满足以下至少一项：
极化方向相同，即CSI-RS发送端口中，将第一极化方向的CSI-RS发送端口划分为一组，将第二极化方向的CSI-RS发送端口划分为另一组，例如：第一极化方向为水平极化方向，第二极化方向为垂直极化方向；或者，第一极化方向为+45度极化方向，第二极化方向为-45度极化方向，在此并不穷举；
对应的发送天线位于同一行，即一组内的CSI-RS发送端口对应的发送天线位于网络侧设备的天线面板上的同一行；
对应的发送天线位于同一列，即一组内的CSI-RS发送端口对应的发送天线位于网络侧设备的天线面板上的同一列；
时域资源相同；
频域资源相同；
码分复用(Code Division Multiple，CDM)方式相同；
包括所述第一信道信息的全部CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数。
一种实施方式中，一组CSI-RS发送端口包括同一时频资源对应的不同CDM的CSI-RS发送端口，或者一组CSI-RS发送端口包括同一CDM对应的全部时频资源上的CSI-RS发送端口。
一种实施方式中，在位于同一组内的CSI-RS发送端口为连续排列的N个CSI-RS发送端口的情况下，第一组CSI-RS发送端口内的CSI-RS发送端口与第二组CSI-RS发送端口内的CSI-RS发送端口可以是顺序或逆序排列的，第一组CSI-RS发送端口和第二组CSI-RS发送端口是所述K组CSI-RS发送端口中相邻的两组CSI-RS发送端口。例如：第一组CSI-RS发送端口包括第0至9个CSI-RS发送端口，第二组CSI-RS发送端口包括第10至16个CSI-RS发送端口。
可选地，N的取值可以是协议约定的，或者是网络侧设备指示的，或者是根据K的取值和目标信道的总CSI-RS发送端口数确定的，例如：假设目标信道包括X个CSI-RS发送端口，则N＝X÷K。
一种实施方式中，网络侧设备可以指示K的取值，协议可以约定位于同一组内的CSI-RS发送端口的极化方向相同，终端可以网络侧设备指示的K，以及协议约定的位于同一组内的CSI-RS发送端口的极化方向相同，来确定每一组CSI-RS发送端口中包含的CSI-RS发送端口数目，以及具体包含哪个或哪些CSI-RS发送端口。
作为一种可选的实施方式，所述信息传输方法还包括：
所述终端根据具有的所述第一AI网络侧模型确定所述第一信息；
所述终端向所述网络侧设备发送第一指示信息，所述第一指示信息指示所述第一信息。
在实施中，终端确定第一信息后，向网络侧设备上报该第一信息，这样，网络侧设备可以根据接收到的第一信息确定每一个信道特征信息是基于哪些CSI-RS发送端口的信道信息确定的，从而恢复这些CSI-RS发送端口的信道信息。
本实施方式中，终端可以根据具有的所述第一AI网络侧模型的输入维度来确定第一信息，以使按照第一信息划分的每一组第二信道信息的维度与该第一AI网络侧模型的输入维度匹配。
作为一种可选的实施方式，在所述终端基于第一信息，从第一信道信息中确定K组第二信道信息之前，所述信息传输方法还包括：
所述终端接收来自所述网络侧设备的第三信息；
其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
一种实施方式中，所述终端获知了所述第一信息和所述第一标识之间的第一关联关系，可以是在协议中约定或网络侧设备提前配置了各种第一信息与其第一标识之间的关联关系。
一种实施方式中，第三信息可以包含在CSI report config中。
本实施方式中，第一信息可以由网络侧设备指示或配置。
作为一种可选的实施方式，所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，包括：
所述终端分别对所述M组第二信道信息进行目标归一化处理，得到M组第三信道信息；
所述终端基于所述M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理，得到M个信道特征信息。
可选地，所述目标归一化处理包括以下至少一项：
最大值归一化处理；
功率归一化处理。
其中，最大值归一化处理可以是将所述M组第二信道信息中的每一个元素分别与幅度最大的元素进行相除，得到最大值归一化处理后的M组第三信道信息。
一种实施方式中，最大值归一化处理可以是对所述M组第二信道信息统一进行归一化处理，此时，从所述M组第二信道信息中选择幅度最大的元素，并分别将所述M组第二信道信息中的每一个元素分别与幅度最大的元素进行相除，得到最大值归一化处理后的M组第三信道信息。
一种实施方式中，最大值归一化处理可以是分别对M组第二信道信息中的每一组进行归一化处理，此时，从一组第二信道信息中选择幅度最大的元素，并将该组第二信道信息中的每一个元素分别与幅度最大的元素进行相除，得到最大值归一化处理后的一组第三 信道信息，并依此循环，得到M组第三信道信息。
上述功率归一化处理，可以是基于功率调整因子对第二信道信息中的元素进行归一化处理，与上述最大值归一化处理相似的，功率归一化处理也可以是以对所述M组第二信道信息统一进行归一化处理，或者是分别对M组第二信道信息中的每一组进行归一化处理，在此不做赘述。
本实施方式中，输入第一AI网络模型的信道信息是归一化处理后的信道矩阵或预编码矩阵，例如：每个CSI-RS发送端口组对应的信道信息在输入第一AI网络模型之前，进行归一化处理，或者，归一化之后的预编码矩阵部分内容，例如32个CSI-RS发送端口对应的32个元素的列向量的部分元素进行功率归一化或最大值归一化之后的结果输入第一AI网络模型，或者，分别将每一组CSI-RS发送端口对应的预编码矩阵进行归一化处理之后的结果输入第一AI网络模型。
需要说明的是，输入第一AI网络模型的信道信息可以是未经归一化处理的信道矩阵或预编码矩阵，例如：将未经归一化处理的信道矩阵输入第一AI网络模型，或者，将未经归一化的预编码矩阵部分内容输入第一AI网络模型，如32个端口对应的预编码矩阵为32个元素的向量，可以直接将其中与一组CSI-RS发送端口对应的系数输入第一AI网络模型，在此不作具体限定。
可选地，所述信息传输方法还包括：
所述终端向所述网络侧设备发送第四信息，所述第四信息包括所述M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系。
在实施中，如果不同组的CSI-RS发送端口的信道信息在输入第一AI网络模型之前分别进行了归一化处理，此时，不同组CSI-RS发送端口的信道信息可能进行不同的归一化处理，则终端需要额外上报每组CSI-RS发送端口之间的幅度和/或相位差(对应最大值归一化处理)，或者功率调整因子(对应功率归一化处理)。
一种实施方式中，在第二信道信息是复数的情况下，第四信息包括所述M组第二信道信息对应的M组CSI-RS发送端口之间的相位和幅度关系。
一种实施方式中，在第二信道信息是实数的情况下，第四信息包括所述M组第二信道信息对应的M组CSI-RS发送端口之间的幅度关系。
一种实施方式中，第四信息可以包括幅度等级，每一个幅度等级对应一个幅度区间，此时，上述M组CSI-RS发送端口之间的幅度关系可以包括M组CSI-RS发送端口之间的幅度等级差。
可选地，在两组CSI-RS发送端口位于同一幅度区间内的情况下，可以不上报这两组CSI-RS发送端口的幅度关系。例如：在第二信道信息是复数的情况下，若两组CSI-RS发送端口位于同一幅度区间内，则可以上报这两组CSI-RS发送端口的相位关系，而不上报这两组CSI-RS发送端口的幅度关系。
本实施方式中，终端还向网络侧设备上报每组CSI-RS发送端口的信道信息之间的幅 度和/或相位差，或者功率调整因子，网络侧设备则可以根据获取到的上述每组CSI-RS发送端口的信道信息之间的幅度和/或相位差，或者功率调整因子，恢复上述第二信道信息的真值。
可选地，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度和/或相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
可选地，所述相位和/或幅度关系包括以下至少一项：
第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
一种实施方式中，上述M组CSI-RS发送端口之间的相位和/或幅度关系，可以是相位差和/或幅度差，例如：第一组CSI-RS发送端口对应的第一组第二信道信息，基于所述第一组第二信道信息中的最强元素W1进行最大值归一化处理，第二组CSI-RS发送端口对应的第二组第二信道信息，基于所述第二组第二信道信息中的最强元素W2进行最大值归一化处理，此时，第一组CSI-RS发送端口和第二组CSI-RS发送端口之间的相位关系包括W1与W2之间的相位差，第一组CSI-RS发送端口和第二组CSI-RS发送端口之间的幅度关系包括W1与W2之间的幅度差。
需要说明的是，在分别对所述M组第二信道信息进行功率归一化处理的情况下，上述相位和/或幅度关系可以包括功率调整因子，其中，功率调整因子可以是一组CSI-RS发送端口的总功率占M组CSI-RS发送端口的总功率的比例。
可选地，所述信息传输方法还包括：
所述终端向所述网络侧设备发送所述第一组CSI-RS发送端口的标识。
本实施方式中，终端告知网络侧设备哪一组CSI-RS发送端口是最强CSI-RS发送端口组，这样，网络侧设备可以基于该最强CSI-RS发送端口组对应的第二信道信息来恢复其他组CSI-RS发送端口的第二信道信息。
作为一种可选的实施方式，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
所述M组第二信道信息对应相同的第一AI网络侧模型；
所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标 第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
一种实施方式中，M组第二信道信息的长度相等，即M组CSI-RS发送端口包括相同数目的CSI-RS发送端口，此时，M组第二信道信息对应相同的第一AI网络侧模型，例如：假设目标信道包括X个CSI-RS发送端口，基站指示或协议约定K的取值，则终端确定每一组CSI-RS发送端口包括X÷K个CSI-RS发送端口，且每X÷K个CSI-RS发送端口的信道信息作为一组，输入至第一AI网络侧模型中，得到该组CSI-RS发送端口对应的信道特征信息。
一种实施方式中，M组第二信道信息对应不同的第一AI网络侧模型，此时，每一组第二信道信息各自对应的第一AI网络侧模型可以由网络侧设备指示或者由终端确定。
可选地，所述信息传输方法还包括：
所述终端向所述网络侧设备发送第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
本实施方式中，在终端确定每一组第二信道信息各自对应的第一AI网络侧模型的情况下，终端将确定的每一组第二信道信息各自对应的第一AI网络侧模型上报给网络侧设备，这样，网络侧设备可以使用解码AI网络模型(即第二AI网络模型)来恢复与之对应的编码AI网络侧模型(即第一AI网络模型)输出的信道特征信息。
一种实施方式中，目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，可以是目标第二信道信息的长度与第一AI网络侧模型的输入维度匹配，此时，若两组第二信道信息的长度相同，则这两组第二信道信息可以对应同一个第一AI网络侧模型。
作为一种可选的实施方式，所述第一信道信息为目标下行信道的同一层(layer)的信道信息，所述目标下行信道的秩大于或等于1。
本实施方式中，每个layer的信道信息独立处理，但是，不同layer的第二信道信息可以使用相同的第一AI网络侧模型。
一种实施方式中，不同layer对应的CSI-RS发送端口的分组信息可以相同，例如：将layer1对应的CSI-RS发送端口划分为3组，将layer2对应的CSI-RS发送端口也划分为3组，此时，layer1的一组CSI-RS发送端口与layer2的一组CSI-RS发送端口可以对应相同的第一AI网络侧模型。
可选地，layer1的3组CSI-RS发送端口，以及layer2的3组CSI-RS发送端口可以对应同一个第一AI网络侧模型。
一种实施方式中，在上报M组CSI-RS发送端口之间的相位和/或幅度关系的情况下，可以分别计算每一个layer内的不同组CSI-RS发送端口之间的相位和/或幅度关系，或者，将目标下行信道的全部layer作为一个整体，计算全部layer的CSI-RS发送端口之间的相位和/或幅度关系。
在本申请实施例中，对CSI-RS发送端口进行分组，每一组CSI-RS发送端口的信道信息采用对应一个AI网络模型进行处理，其中，一个信道的第一信道信息能够划分为K组，且每一个AI网络模型仅输入对应的一组CSI-RS发送端口的信道信息，而无需输入全部CSI-RS发送端口的信道信息，这样，低CSI-RS发送端口数的AI网络模型能够用于处理高CSI-RS发送端口数的信道信息，从而提高了AI网络模型的复用效率和灵活度。
请参阅图3，本申请实施例提供的信息处理方法，其执行主体可以是网络侧设备，如图3所示，该信息处理方法可以包括以下步骤：
步骤301、网络侧设备接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息。
其中，上述第二信息与如图2所示方法实施例中的第二信息的含义相同，在此不再赘述。
步骤302、所述网络侧设备根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数。
其中，上述第一信息与如图2所示方法实施例中的第一信息的含义相同，所述网络侧设备用于根据第一信息确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，得到所述信道特征信息的第一AI网络模型，与该信道特征信息对应的第二AI网络侧模型，为相互匹配的AI网络模型或联合训练得到的AI网络模型，如：第一AI网络模型为编码AI网络模型或AI网络模型的编码部分，第二AI网络模型为解码AI网络模型或AI网络模型的解码部分。
步骤303、所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
其中，第二处理可以包括解码、解压缩、解量化处理中的至少一项。
作为一种可选的实施方式，所述第一信息包括以下至少一项：
K的取值；
每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
作为一种可选的实施方式，所述第一信息满足以下至少一项：
由所述网络侧设备指示；
由所述终端选择；
由协议约定。
作为一种可选的实施方式，位于同一组内的CSI-RS发送端口满足以下至少一项：
极化方向相同；
对应的发送天线位于同一行；
对应的发送天线位于同一列；
时域资源相同；
频域资源相同；
码分复用CDM方式相同；
包括第一信道信息的CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数，所述第一信道信息是所述M组第二信道信息对应的信道的全部CSI-RS发送端口的信道信息。
作为一种可选的实施方式，所述第一信道信息包括以下至少一项：
原始的信道矩阵或向量；
预编码矩阵或向量；
预处理后的信道矩阵或向量；
预处理后的预编码矩阵或向量。
一种实施方式中，在第一信道信息是一个layer的信道信息的情况下，该layer的信道信息对应的信道矩阵只有一列，此时，该第二信道信息可以称之为向量。当然，在第二信道信息包括至少两个layer的信道信息的情况下，也可以通过预处理将该至少两个layer的信道信息处理成向量，在此不作具体阐述。
作为一种可选的实施方式，一组第二信道信息包括以下至少一项：
一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
可选地，所述信息处理方法还包括：
所述网络侧设备根据所述M组第二信道信息，确定所述第一信道信息或所述第一信道信息对应的预编码矩阵。
一种实施方式中，网络侧设备可以根据所述M组第二信道信息，拼接得到所述第一预编码矩阵。
作为一种可选的实施方式，在所述网络侧设备根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型之前，所述信息处理方法还包括：
所述网络侧设备接收来自所述终端的第一指示信息，所述第一指示信息指示所述第一信息。
作为一种可选的实施方式，在所述网络侧设备接收来自终端的第二信息之前，所述信息处理方法还包括：
所述网络侧设备向所述终端发送第三信息；
其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
作为一种可选的实施方式，所述M个信道特征信息是基于M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理得到的信道特征信息，所述M组第三信道信息是对M组第二信道信息进行目标归一化处理后得到的信道信息。
作为一种可选的实施方式，所述目标归一化处理包括以下至少一项：
最大值归一化处理；
功率归一化处理。
作为一种可选的实施方式，所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息，包括：
所述网络侧设备接收来自所述终端的第四信息，所述第四信息包括M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系；
所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第三信道信息；
所述网络侧设备根据所述第四信息，将所述M组第三信道信息处理成所述M组第二信道信息。
一种实施方式中，所述网络侧设备根据所述第四信息，将所述M组第三信道信息处理成所述M组第二信道信息，可以是网络侧设备根据所述第四信息，对所述M组第三信道信息进行目标归一化处理的逆处理，以恢复目标归一化处理之前的M组第二信道信息。
作为一种可选的实施方式，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
作为一种可选的实施方式，所述信息处理方法还包括：
所述网络侧设备接收来自所述终端的所述第一组CSI-RS发送端口的标识。
作为一种可选的实施方式，所述相位和/或幅度关系包括以下至少一项：
第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
作为一种可选的实施方式，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
所述M组第二信道信息对应相同的第一AI网络侧模型；
所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
作为一种可选的实施方式，所述信息处理方法还包括：
所述网络侧设备接收来自所述终端的第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
本实施方式中，终端选择并上报所述M组第二信道信息各自对应的第一AI网络模型，这样，网络侧设备可以根据所述M组第二信道信息各自对应的第一AI网络模型，确定所述M组第二信道信息各自对应的第二AI网络模型，其中，同一组第二信道信息对应的第一AI网络模型和第二AI网络模型为相互匹配或联合训练得到的编码和解码AI网络模型。
作为一种可选的实施方式，所述第一信道信息为目标下行信道的同一层的信道信息，所述目标下行信道的秩大于或等于1。
本申请实施例中，网络侧设备接收来自终端的M个信道特征信息，并根据第一信息，确定该M个信道特征信息各自对应的第二AI网络模型，从而使用第二AI网络模型将对应的信道特征信息恢复成第二信道信息，从而实现信道特征信息的接收和恢复过程，其中，第二AI网络模型的输入为部分CSI-RS发送端口的信道特征信息，使得第二AI网络模型的模型尺寸较小，此外，通过将相同数目的CSI-RS发送端口划分为一组的方式，可以实现相同的第二AI网络模型可重复用于不同CSI-RS发送端口数的信道，这样，低CSI-RS发送端口数的AI网络模型能够用于处理高CSI-RS发送端口数的信道信息，从而提高了AI网络模型的复用效率和灵活度。
为了便于说明，以基站天线配置为2×8，即水平方向8个天线，垂直方向两个天线，组成一个长方形天线阵列，其中，每个天线都是双极化天线，形成32个CSI-RS发送端口，终端有4个接收天线的应用场景为例，对本申请实施例提供的信息传输方法和信息处理方法进行举例说明：
实施例一
若终端的第一AI网络模型是16个CSI-RS发送端口输入的AI模型，终端通过信道估计可以获得4×32的信道矩阵，将其分割为两个4×16的信道矩阵，对应每个极化方向，分别计算对应的预编码矩阵，得到两个16×1的预编码矩阵，分别将这两个16×1的 预编码矩阵输入AI模型，具体的AI处理方式不做限定，可以是有预处理的，如在把数据输入模型之前，可以对数据进行计算，把16×1的矩阵预处理成8×1的矩阵，可以是每个子带独立的，如13个子带各自做分组，可以是全子带一起或部分子带一起，如13个子带一起做分组等。
例如：假设H＝[H1 H2]，其中H表示4×32的信道矩阵，H1和H2分别对应两个极化的信道矩阵，分别计算H1对应的预编码矩阵W1，以及计算H2对应的预编码矩阵W2：
终端计算两组CSI-RS发送端口之间的幅度和相位差：
方式一：计算W1中的幅度最大的系数与W2中的幅度最大的系数之间的幅度差和相位差，并将计算得到的幅度差和相位差进行量化处理之后上报基站。
方式二：计算W1对应的等效信道矩阵H1W1，以及和W2对应的等效信道矩阵H2W2，计算两个等效信道的功率比例并上报基站。
实施例二
对于32个CSI-RS发送端口，命令类型(cmd-Type)指示为：cmd8-FD2-TD4，即CDM组(group)中有8个CDM方式，具有2个频域位置，4个时域位置，则可以按照以下方式对CSI-RS发送端口进行分组：
方式一：分成8组，每个CDM一组，每组4个端口，分别将每一组CSI-RS发送端口的第二信道信息输入对应的AI模型，得到8个信道特征信息；
方式二：分成4组，每个时频位置一组，每组8个端口，分别将每一组CSI-RS发送端口的第二信道信息输入对应的AI模型，得到4组信道特征信息。
本申请实施例提供的信息传输方法，执行主体可以为信息传输装置。本申请实施例中以信息传输装置执行信息传输方法为例，说明本申请实施例提供的信息传输装置。
请参阅图4，本申请实施例提供的一种信息传输装置，可以是终端内的装置，如图4所示，该信息传输装置400可以包括以下模块：
第一确定模块401，用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
第一处理模块402，用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
第一发送模块403，用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
可选地，所述第一信息包括以下至少一项：
K的取值；
每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
可选地，所述第一信息满足以下至少一项：
由所述网络侧设备指示；
由所述终端选择；
由协议约定。
可选地，位于同一组内的CSI-RS发送端口满足以下至少一项：
极化方向相同；
对应的发送天线位于同一行；
对应的发送天线位于同一列；
时域资源相同；
频域资源相同；
码分复用CDM方式相同；
包括所述第一信道信息的全部CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数。
可选地，所述第一信道信息包括以下至少一项：
原始的信道矩阵或向量；
预编码矩阵或向量；
预处理后的信道矩阵或向量；
预处理后的预编码矩阵或向量。
可选地，一组第二信道信息包括以下至少一项：
一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
可选地，所述信息传输装置400还包括：
第三确定模块，用于根据具有的所述第一AI网络侧模型确定所述第一信息；
第二发送模块，用于向所述网络侧设备发送第一指示信息，所述第一指示信息指示所述第一信息。
可选地，所述信息传输装置400还包括：
第二接收模块，用于接收来自所述网络侧设备的第三信息；
其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
可选地，第一处理模块402，包括：
第一处理单元，用于分别对所述M组第二信道信息进行目标归一化处理，得到M组第三信道信息；
第二处理单元，用于基于所述M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理，得到M个信道特征信息。
可选地，所述目标归一化处理包括以下至少一项：
最大值归一化处理；
功率归一化处理。
可选地，所述信息传输装置400还包括：
第三发送模块，用于向所述网络侧设备发送第四信息，所述第四信息包括所述M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系。
可选地，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度和/或相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
可选地，所述信息传输装置400还包括：
第四发送模块，用于向所述网络侧设备发送所述第一组CSI-RS发送端口的标识。
可选地，所述相位和/或幅度关系包括以下至少一项：
第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
可选地，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
所述M组第二信道信息对应相同的第一AI网络侧模型；
所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
可选地，所述信息传输装置400还包括：
第五发送模块，用于向所述网络侧设备发送第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
可选地，所述第一信道信息为目标下行信道的同一层的信道信息，所述目标下行信道 的秩大于或等于1。
本申请实施例中的信息传输装置可以是电子设备，例如具有操作系统的电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以是终端，也可以为除终端之外的其他设备。示例性的，终端可以包括但不限于上述所列举的终端11的类型，其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。
本申请实施例提供的信息传输装置400，能够实现如图2所示方法实施例中终端实现的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。
本申请实施例提供的信息处理方法，执行主体可以为信息处理装置。本申请实施例中以信息处理装置执行信息处理方法为例，说明本申请实施例提供的信息处理装置。
请参阅图5，本申请实施例提供的一种信息处理装置，可以是网络侧设备内的装置，如图5所示，该信息处理装置500可以包括以下模块：
第一接收模块501，用于接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；
第二确定模块502，用于根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；
第二处理模块503，用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
可选地，所述第一信息包括以下至少一项：
K的取值；
每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
可选地，所述第一信息满足以下至少一项：
由所述网络侧设备指示；
由所述终端选择；
由协议约定。
可选地，位于同一组内的CSI-RS发送端口满足以下至少一项：
极化方向相同；
对应的发送天线位于同一行；
对应的发送天线位于同一列；
时域资源相同；
频域资源相同；
码分复用CDM方式相同；
包括第一信道信息的CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数，所述第一信道信息是所述M组第二信道信息对应的信道的全部CSI-RS发送端口的信道信息。
可选地，所述第一信道信息包括以下至少一项：
原始的信道矩阵或向量；
预编码矩阵或向量；
预处理后的信道矩阵或向量；
预处理后的预编码矩阵或向量。
可选地，一组第二信道信息包括以下至少一项：
一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
可选地，信息处理装置500还包括：
第四确定模块，用于根据所述M组第二信道信息，确定所述第一信道信息或所述第一信道信息对应的预编码矩阵。
可选地，信息处理装置500还包括：
第三接收模块，用于接收来自所述终端的第一指示信息，所述第一指示信息指示所述第一信息。
可选地，信息处理装置500还包括：
第六发送模块，用于向所述终端发送第三信息；
其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
可选地，所述M个信道特征信息是基于M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理得到的信道特征信息，所述M组第三信道信息是对M组第二信道信息进行目标归一化处理后得到的信道信息。
可选地，所述目标归一化处理包括以下至少一项：
最大值归一化处理；
功率归一化处理。
可选地，第二处理模块503，包括：
接收单元，用于接收来自所述终端的第四信息，所述第四信息包括M组第二信道信 息对应的M组CSI-RS发送端口之间的相位和/或幅度关系；
第三处理单元，用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第三信道信息；
第四处理单元，用于根据所述第四信息，将所述M组第三信道信息处理成所述M组第二信道信息。
可选地，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
第一CSI-RS发送端口组与第二CSI-RS发送端口组的幅度相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
可选地，信息处理装置500还包括：
第四接收模块，用于接收来自所述终端的所述第一组CSI-RS发送端口的标识。
可选地，所述相位和/或幅度关系包括以下至少一项：
第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
可选地，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
所述M组第二信道信息对应相同的第一AI网络侧模型；
所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
可选地，信息处理装置500还包括：
第五接收模块，用于接收来自所述终端的第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
可选地，所述第一信道信息为目标下行信道的同一层的信道信息，所述目标下行信道的秩大于或等于1。
本申请实施例提供的信息处理装置500，能够实现如图3所示方法实施例中网络侧设备实现的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。
可选的，如图6所示，本申请实施例还提供一种通信设备600，包括处理器601和存储器602，存储器602上存储有可在所述处理器601上运行的程序或指令，例如，该通信 设备600为终端时，该程序或指令被处理器601执行时实现如图2所示方法实施例的各个步骤，且能达到相同的技术效果。该通信设备600为网络侧设备时，该程序或指令被处理器601执行时实现如图3所示方法实施例的各个步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供一种终端，包括处理器和通信接口，所述处理器用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；所述处理器还用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；所述通信接口用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
该终端实施例能够实现如图4所示信息传输装置400执行的各个过程，且能达到相同的技术效果，在此不再赘述。具体地，图7为实现本申请实施例的一种终端的硬件结构示意图。
该终端700包括但不限于：射频单元701、网络模块702、音频输出单元703、输入单元704、传感器705、显示单元706、用户输入单元707、接口单元708、存储器709以及处理器710等中的至少部分部件。
本领域技术人员可以理解，终端700还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器710逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图7中示出的终端结构并不构成对终端的限定，终端可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
应理解的是，本申请实施例中，输入单元704可以包括图形处理单元(Graphics Processing Unit，GPU)7041和麦克风7042，图形处理器7041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元706可包括显示面板7061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板7061。用户输入单元707包括触控面板7071以及其他输入设备7072中的至少一种。触控面板7071，也称为触摸屏。触控面板7071可包括触摸检测装置和触摸控制器两个部分。其他输入设备7072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
本申请实施例中，射频单元701接收来自网络侧设备的下行数据后，可以传输给处理器710进行处理；另外，射频单元701可以向网络侧设备发送上行数据。通常，射频单元701包括但不限于天线、放大器、收发信机、耦合器、低噪声放大器、双工器等。
存储器709可用于存储软件程序或指令以及各种数据。存储器709可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至 少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器709可以包括易失性存储器或非易失性存储器，或者，存储器709可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请实施例中的存储器709包括但不限于这些和任意其它适合类型的存储器。
处理器710可包括一个或多个处理单元；可选地，处理器710集成应用处理器和调制解调处理器，其中，应用处理器主要处理涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器710中。
其中，处理器710，用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
处理器710，还用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
射频单元701，用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
可选地，所述第一信息包括以下至少一项：
K的取值；
每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
可选地，所述第一信息满足以下至少一项：
由所述网络侧设备指示；
由所述终端选择；
由协议约定。
可选地，位于同一组内的CSI-RS发送端口满足以下至少一项：
极化方向相同；
对应的发送天线位于同一行；
对应的发送天线位于同一列；
时域资源相同；
频域资源相同；
码分复用CDM方式相同；
包括所述第一信道信息的全部CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数。
可选地，所述第一信道信息包括以下至少一项：
原始的信道矩阵或向量；
预编码矩阵或向量；
预处理后的信道矩阵或向量；
预处理后的预编码矩阵或向量。
可选地，一组第二信道信息包括以下至少一项：
一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
可选地，处理器710，还用于根据具有的所述第一AI网络侧模型确定所述第一信息；
射频单元701，还用于向所述网络侧设备发送第一指示信息，所述第一指示信息指示所述第一信息。
可选地，在处理器710执行所述基于第一信息，从第一信道信息中确定K组第二信道信息之前，射频单元701，还用于接收来自所述网络侧设备的第三信息；
其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
可选地，处理器710执行的所述基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，包括：
分别对所述M组第二信道信息进行目标归一化处理，得到M组第三信道信息；
基于所述M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理，得到M个信道特征信息。
可选地，所述目标归一化处理包括以下至少一项：
最大值归一化处理；
功率归一化处理。
可选地，射频单元701，还用于向所述网络侧设备发送第四信息，所述第四信息包括 所述M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系。
可选地，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度和/或相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
可选地，射频单元701，还用于向所述网络侧设备发送所述第一组CSI-RS发送端口的标识。
可选地，所述相位和/或幅度关系包括以下至少一项：
第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
可选地，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
所述M组第二信道信息对应相同的第一AI网络侧模型；
所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
可选地，射频单元701，还用于向所述网络侧设备发送第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
可选地，所述第一信道信息为目标下行信道的同一层的信道信息，所述目标下行信道的秩大于或等于1。
本申请实施例提供的终端700能够实现如图4所示信息传输装置执行的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。
本申请实施例还提供一种网络侧设备，包括处理器和通信接口，所述通信接口用于接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；所述处理器用于根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发 送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；所述处理器还用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
该网络侧设备实施例能够实现如图5所示信息处理装置500执行的各个过程，且能达到相同的技术效果，在此不再赘述。具体地，本申请实施例还提供了一种网络侧设备。如图8所示，该网络侧设备800包括：天线801、射频装置802、基带装置803、处理器804和存储器805。天线801与射频装置802连接。在上行方向上，射频装置802通过天线801接收信息，将接收的信息发送给基带装置803进行处理。在下行方向上，基带装置803对要发送的信息进行处理，并发送给射频装置802，射频装置802对收到的信息进行处理后经过天线801发送出去。
以上实施例中网络侧设备执行的方法可以在基带装置803中实现，该基带装置803包括基带处理器。
基带装置803例如可以包括至少一个基带板，该基带板上设置有多个芯片，如图8所示，其中一个芯片例如为基带处理器，通过总线接口与存储器805连接，以调用存储器805中的程序，执行以上方法实施例中所示的网络设备操作。
该网络侧设备还可以包括网络接口806，该接口例如为通用公共无线接口(Common Public Radio Interface，CPRI)。
具体地，本申请实施例的网络侧设备800还包括：存储在存储器805上并可在处理器804上运行的指令或程序，处理器804调用存储器805中的指令或程序执行图5所示各模块执行的方法，并达到相同的技术效果，为避免重复，故不在此赘述。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的终端中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器ROM、随机存取存储器RAM、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。
本申请实施例另提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供了一种通信系统，包括：终端和网络侧设备，所述终端可用于执 行如图2所示的信息传输方法的步骤，所述网络侧设备可用于执行如图3所示的信息处理方法的步骤。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (38)

  1. 一种信息传输方法，包括：
    终端基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
    所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
    所述终端向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
  2. 根据权利要求1所述的方法，其中，所述第一信息包括以下至少一项：
    K的取值；
    每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
    每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
  3. 根据权利要求1所述的方法，其中，所述第一信息满足以下至少一项：
    由所述网络侧设备指示；
    由所述终端选择；
    由协议约定。
  4. 根据权利要求1所述的方法，其中，位于同一组内的CSI-RS发送端口满足以下至少一项：
    极化方向相同；
    对应的发送天线位于同一行；
    对应的发送天线位于同一列；
    时域资源相同；
    频域资源相同；
    码分复用CDM方式相同；
    包括所述第一信道信息的全部CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数。
  5. 根据权利要求1所述的方法，其中，所述第一信道信息包括以下至少一项：
    原始的信道矩阵或向量；
    预编码矩阵或向量；
    预处理后的信道矩阵或向量；
    预处理后的预编码矩阵或向量。
  6. 根据权利要求5所述的方法，其中，一组第二信道信息包括以下至少一项：
    一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
    第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
    第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
  7. 根据权利要求1至6中任一项所述的方法，所述方法还包括：
    所述终端根据具有的所述第一AI网络侧模型确定所述第一信息；
    所述终端向所述网络侧设备发送第一指示信息，所述第一指示信息指示所述第一信息。
  8. 根据权利要求1至6中任一项所述的方法，其中，在所述终端基于第一信息，从第一信道信息中确定K组第二信道信息之前，所述方法还包括：
    所述终端接收来自所述网络侧设备的第三信息；
    其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
  9. 根据权利要求1至6中任一项所述的方法，其中，所述终端基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，包括：
    所述终端分别对所述M组第二信道信息进行目标归一化处理，得到M组第三信道信息；
    所述终端基于所述M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理，得到M个信道特征信息。
  10. 根据权利要求9所述的方法，其中，所述目标归一化处理包括以下至少一项：
    最大值归一化处理；
    功率归一化处理。
  11. 根据权利要求9所述的方法，所述方法还包括：
    所述终端向所述网络侧设备发送第四信息，所述第四信息包括所述M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系。
  12. 根据权利要求11所述的方法，其中，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
    第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度和/或相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
  13. 根据权利要求12所述的方法，所述方法还包括：
    所述终端向所述网络侧设备发送所述第一组CSI-RS发送端口的标识。
  14. 根据权利要求12所述的方法，其中，所述相位和/或幅度关系包括以下至少一项：
    第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
    第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
    所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
  15. 根据权利要求1至6中任一项所述的方法，其中，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
    所述M组第二信道信息对应相同的第一AI网络侧模型；
    所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
    目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
  16. 根据权利要求1至6中任一项所述的方法，所述方法还包括：
    所述终端向所述网络侧设备发送第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
  17. 根据权利要求1至6中任一项所述的方法，其中，所述第一信道信息为目标下行信道的同一层的信道信息，所述目标下行信道的秩大于或等于1。
  18. 一种信息处理方法，包括：
    网络侧设备接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；
    所述网络侧设备根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；
    所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
  19. 根据权利要求18所述的方法，其中，所述第一信息包括以下至少一项：
    K的取值；
    每一组CSI-RS发送端口内的CSI-RS发送端口的数目；
    每一组CSI-RS发送端口内的CSI-RS发送端口的标识。
  20. 根据权利要求18所述的方法，其中，所述第一信息满足以下至少一项：
    由所述网络侧设备指示；
    由所述终端选择；
    由协议约定。
  21. 根据权利要求18所述的方法，其中，位于同一组内的CSI-RS发送端口满足以下至少一项：
    极化方向相同；
    对应的发送天线位于同一行；
    对应的发送天线位于同一列；
    时域资源相同；
    频域资源相同；
    码分复用CDM方式相同；
    包括第一信道信息的CSI-RS发送端口中连续排列的N个CSI-RS发送端口，N为大于或等于1的整数，所述第一信道信息是所述M组第二信道信息对应的信道的全部CSI-RS发送端口的信道信息。
  22. 根据权利要求21所述的方法，其中，所述第一信道信息包括以下至少一项：
    原始的信道矩阵或向量；
    预编码矩阵或向量；
    预处理后的信道矩阵或向量；
    预处理后的预编码矩阵或向量。
  23. 根据权利要求22所述的方法，其中，一组第二信道信息包括以下至少一项：
    一组CSI-RS发送端口对应的原始信道矩阵，所述一组CSI-RS发送端口与所述一组第二信道信息对应；
    第一预编码矩阵中与所述一组CSI-RS发送端口对应的元素构成的矩阵，所述第一预编码矩阵为所述第一信道信息的预编码矩阵；
    第二预编码矩阵，所述第二预编码矩阵为所述一组第二信道信息的预编码矩阵。
  24. 根据权利要求23所述的方法，所述方法还包括：
    所述网络侧设备根据所述M组第二信道信息，确定所述第一信道信息或所述第一信道信息对应的预编码矩阵。
  25. 根据权利要求18至23中任一项所述的方法，其中，在所述网络侧设备根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型之前，所述方法还包括：
    所述网络侧设备接收来自所述终端的第一指示信息，所述第一指示信息指示所述第一信息。
  26. 根据权利要求18至23中任一项所述的方法，其中，在所述网络侧设备接收来自终端的第二信息之前，所述方法还包括：
    所述网络侧设备向所述终端发送第三信息；
    其中，所述第三信息指示或配置所述第一信息，或者，所述第三信息指示第一标识，所述第一标识与所述第一信息关联，且所述终端获知了所述第一信息和所述第一标识之间的第一关联关系。
  27. 根据权利要求18至23中任一项所述的方法，其中，所述M个信道特征信息是基于M组第三信道信息各自对应的第一AI网络模型对所述M组第三信道信息进行第一处理得到的信道特征信息，所述M组第三信道信息是对M组第二信道信息进行目标归一化处理后得到的信道信息。
  28. 根据权利要求27所述的方法，其中，所述目标归一化处理包括以下至少一项：
    最大值归一化处理；
    功率归一化处理。
  29. 根据权利要求27所述的方法，其中，所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息，包括：
    所述网络侧设备接收来自所述终端的第四信息，所述第四信息包括M组第二信道信息对应的M组CSI-RS发送端口之间的相位和/或幅度关系；
    所述网络侧设备基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第三信道信息；
    所述网络侧设备根据所述第四信息，将所述M组第三信道信息处理成所述M组第二信道信息。
  30. 根据权利要求29所述的方法，其中，所述M组CSI-RS发送端口之间的相位和/或幅度关系，包括：
    第一组CSI-RS发送端口与第二组CSI-RS发送端口的幅度相位差，所述第一组CSI-RS发送端口是所述M组CSI-RS发送端口中的最强CSI-RS发送端口所在的组或者是所述M组CSI-RS发送端口中的最强CSI-RS发送端口组，所述第二组CSI-RS发送端口是所述M组CSI-RS发送端口中处了所述第一组CSI-RS发送端口之外的每一组CSI-RS发送端口。
  31. 根据权利要求30所述的方法，所述方法还包括：
    所述网络侧设备接收来自所述终端的所述第一组CSI-RS发送端口的标识。
  32. 根据权利要求30所述的方法，其中，所述相位和/或幅度关系包括以下至少一项：
    第一CSI-RS发送端口与第二CSI-RS发送端口之间的相位差，所述第一CSI-RS发送端口为所述第一组CSI-RS发送端口中的最强CSI-RS发送端口，所述第二CSI-RS发送端口包括所述第二组CSI-RS发送端口中的最强CSI-RS发送端口；
    第一CSI-RS发送端口与第二CSI-RS发送端口之间的幅度差；
    所述第一组CSI-RS发送端口与所述第二组CSI-RS发送端口之间的功率调整因子。
  33. 根据权利要求18至23中任一项所述的方法，其中，所述第二信道信息与所述第一AI网络侧模型之间的对应关系满足以下至少一项：
    所述M组第二信道信息对应相同的第一AI网络侧模型；
    所述网络侧设备指示所述M组第二信道信息各自对应的第一AI网络侧模型；
    目标第二信道信息对应的CSI-RS发送端口组中的CSI-RS发送端口数，与所述目标第二信道信息对应的第一AI网络侧模型的输入维度匹配，所述M组第二信道信息包括所述目标第二信道信息。
  34. 根据权利要求18至23中任一项所述的方法，所述方法还包括：
    所述网络侧设备接收来自所述终端的第三指示信息，所述第三指示信息指示所述M组第二信道信息各自对应的第一AI网络模型。
  35. 一种信息传输装置，应用于终端，所述装置包括：
    第一确定模块，用于基于第一信息，从第一信道信息中确定K组第二信道信息，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，所述K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，K为大于或等于1的整数；
    第一处理模块，用于基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理，得到M个信道特征信息，所述K组第二信道信息包括所述M组第二信道信息；
    第一发送模块，用于向网络侧设备发送第二信息，所述第二信息包括所述M个信道特征信息。
  36. 一种信息处理装置，应用于网络侧设备，所述装置包括：
    第一接收模块，用于接收来自终端的第二信息，其中，所述第二信息包括M个信道特征信息，所述M个信道特征信息是基于M组第二信道信息各自对应的第一AI网络模型对所述M组第二信道信息进行第一处理得到的信道特征信息；
    第二确定模块，用于根据第一信息，确定所述M个信道特征信息各自对应的第二AI网络侧模型，其中，所述第一信息包括K组信道状态信息参考信号CSI-RS发送端口的分组信息，K组第二信道信息与所述K组CSI-RS发送端口一一对应，所述K组CSI-RS发送端口中的每一组CSI-RS发送端口包括至少一个CSI-RS发送端口，所述K组第二信道信息包括所述M组第二信道信息，K和M为大于或等于1的整数；
    第二处理模块，用于基于所述M个信道特征信息各自对应的第二AI网络侧模型对所述M个信道特征信息进行第二处理，得到所述M组第二信道信息。
  37. 一种通信设备，包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1至17中任一项所述的信息传输方法的步骤，或者实现如权利要求18至34中任一项所述的信息处理方法的步骤。
  38. 一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1至17中任一项所述的信息传输方法的步骤，或者实现如权利要求18至34中任一项所述的信息处理方法的步骤。
PCT/CN2023/125561 2022-10-27 2023-10-20 信息传输方法、信息处理方法、装置和通信设备 WO2024088162A1 (zh)

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