Information sending method and device, information receiving method and device, communication device and storage medium

Abstract

The invention relates to an information sending method and device, an information receiving method and device, a communication device and a storage medium, and the information sending method comprises the steps: carrying out the preprocessing of estimated full-channel information according to a first number of spatial domain base vectors and a second number of frequency domain base vectors, so as to obtain effective channel information; calculating a feature vector of the effective channel information; and inputting the feature vector into a model to obtain CSI-related information, and sending the CSI-related information to a network device. According to the method and the device, the feature vector of the effective channel information can be further calculated, so that the dimension of the feature vector is irrelevant to the number Nr of the receiving antenna ports. Therefore, the feature vector is used as the input of the model, different models do not need to be trained in a scene with different numbers of receiving antenna ports, the number of the models needing to be trained is reduced, the number of the models needing to be sent to the terminal and the number of the models needing to be stored by the terminal are reduced, the signaling overhead is saved, and the user experience is improved. The storage space of the terminal is saved.

Description

Information sending and receiving method and device, communication device and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular, to an information sending method, an information receiving method, an information sending device, an information receiving device, a communication device, and a computer-readable storage medium. Background Art

[0002] The terminal can report channel state information (CSI) to the network device so that the network device can determine the current channel status and make appropriate configurations to ensure good communication effects.

[0003] In order to reduce the overhead of reporting CSI and improve the accuracy of CSI reporting, relevant technologies have proposed a bilateral model based on artificial intelligence (AI) / machine learning (ML) to determine the CSI generation part and the CSI recovery part to achieve the above purpose.

[0004] However, the input of the model in the related technology is data related to the number of antenna ports. Using this data as the input of the model, different models need to be trained when facing scenarios with different numbers of antenna ports, resulting in the need to configure a large number of models for the terminal, increasing signaling overhead and occupying too much storage space of the terminal.

[0005] Summary of the Invention

[0006] In view of this, the embodiments of the present disclosure propose an information sending method, an information receiving method, an information sending device, an information receiving device, a communication device and a computer-readable storage medium to solve the technical problems in the related art.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for sending information is proposed, which is executed by a terminal. The method includes: preprocessing the estimated full channel information according to a first number of spatial basis vectors and a second number of frequency domain basis vectors to obtain effective channel information; calculating a eigenvector of the effective channel information; inputting the eigenvector into a first artificial intelligence and / or machine learning model to obtain CSI-related information, and sending the CSI-related information to a network device.

[0008] According to a second aspect of an embodiment of the present disclosure, an information receiving method is proposed, which is executed by a network device. The method includes: receiving CSI-related information sent by a terminal using the above-mentioned information sending method.

[0009] According to a third aspect of an embodiment of the present disclosure, an information sending device is proposed, comprising: a processing module configured to preprocess estimated full channel information based on a first number of spatial basis vectors and a second number of frequency domain basis vectors to obtain effective channel information; calculate a eigenvector of the effective channel information; input the eigenvector into a first artificial intelligence and / or machine learning model to obtain CSI-related information; and a sending module configured to send the CSI-related information to a network device.

[0010] According to a fourth aspect of an embodiment of the present disclosure, an information receiving device is proposed, comprising: a receiving module configured to receive CSI-related information sent by the terminal using the above-mentioned information sending method.

[0011] According to a fifth aspect of an embodiment of the present disclosure, an information sending and receiving system is proposed, comprising a terminal and a network side device, wherein the terminal is configured to implement the above-mentioned information sending method, and the network device is configured to implement the above-mentioned information receiving method.

[0012] According to a sixth aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: a processor; and a memory for storing a computer program; wherein, when the computer program is executed by the processor, the above-mentioned information sending method is implemented.

[0013] According to a seventh aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: a processor; and a memory for storing a computer program; wherein, when the computer program is executed by the processor, the above-mentioned information receiving method is implemented.

[0014] According to an eighth aspect of an embodiment of the present disclosure, a computer-readable storage medium is proposed for storing a computer program. When the computer program is executed by a processor, the above-mentioned information sending method is implemented.

[0015] According to a ninth aspect of an embodiment of the present disclosure, a computer-readable storage medium is proposed for storing a computer program. When the computer program is executed by a processor, the above-mentioned information receiving method is implemented.

[0016] According to an embodiment of the present disclosure, a feature vector of effective channel information can be further calculated, for example, by performing eigenvalue analysis on the effective channel information to obtain a feature vector. In this case, the dimension of the feature vector is independent of the number of receiving antenna ports Nr. Thus, by using the feature vector as input to the first artificial intelligence and / or machine learning model, there is no need to train different models based on different numbers of receiving antenna ports. This helps reduce the number of models that need to be trained, thereby reducing the number of models that need to be sent to the terminal and the number of models that need to be stored by the terminal, saving signaling overhead and terminal storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] FIG1 is a schematic diagram showing an application scenario according to an embodiment of the present disclosure.

[0019] FIG2 is a schematic flowchart of a method for sending information according to an embodiment of the present disclosure.

[0020] FIG3 is a schematic flowchart showing another information sending method according to an embodiment of the present disclosure.

[0021] FIG4 is a schematic flowchart showing another information sending method according to an embodiment of the present disclosure.

[0022] FIG5 is a schematic flowchart showing another information sending method according to an embodiment of the present disclosure.

[0023] FIG6 is a schematic flowchart showing a method for receiving information according to an embodiment of the present disclosure.

[0024] FIG7 is a schematic block diagram of an information sending device according to an embodiment of the present disclosure.

[0025] FIG8 is a schematic block diagram of an information receiving device according to an embodiment of the present disclosure.

[0026] FIG9 is a schematic block diagram showing an apparatus for receiving information according to an embodiment of the present disclosure.

[0027] FIG10 is a schematic block diagram of an apparatus for sending information according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0029] The terms used in the embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present disclosure. The singular forms "a," "an," and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0031] For the purposes of brevity and ease of understanding, the terms "greater than," "less than," "higher than," and "lower than" are used herein to describe magnitude relationships. However, those skilled in the art will understand that the term "greater than" also encompasses the meaning of "greater than or equal to," and "less than" also encompasses the meaning of "less than or equal to," and the term "higher than" also encompasses the meaning of "higher than or equal to," and "lower than" also encompasses the meaning of "lower than or equal to."

[0032] The terminal can report channel state information (CSI) to the network device so that the network device can determine the current channel status and make appropriate configurations to ensure good communication effects.

[0033] In order to reduce the overhead of reporting CSI and improve the accuracy of CSI reporting, relevant technologies have proposed a bilateral model based on artificial intelligence (AI) / machine learning (ML) to determine the CSI generation part and the CSI recovery part to achieve the above purpose.

[0034] However, the input of the model in the related technology is data related to the number of antenna ports. Using this data as the input of the model, different models need to be trained when facing scenarios with different numbers of antenna ports, resulting in the need to configure a large number of models for the terminal, increasing signaling overhead and occupying too much storage space of the terminal.

[0035] FIG1 is a schematic diagram showing an application scenario according to an embodiment of the present disclosure.

[0036] As shown in Figure 1, an AI / ML-based CSI generation model is set on the terminal side, and an AI / ML-based CSI recovery model is set on the network device side.

[0037] The terminal determines the input data by detecting the downlink channel information sent by the network device and inputs the input data into the CSI generation model. The CSI generation model can compress the input data and quantize the compressed data (for example, to obtain a binary bit stream) and send it to the network device.

[0038] The network device inputs the quantized data received from the terminal into the CSI recovery model. The CSI recovery model can recover the quantized data to obtain recovered input data, such as CSI-related information.

[0039] However, the dimension of the input data determined by the terminal is related to the port configuration of the transmitting antenna (network-side antenna) of the network device and the number of frequency domain units. Specifically, different CSI generation partial models need to be trained for different transmitting antenna port configurations. The CSI generation partial model needs to be configured by the network device to the terminal, and the terminal needs to store it. Too many CSI generation partial models will increase signaling overhead and occupy the storage space of the terminal.

[0040] To address the above issues, the input data can be preprocessed using the spatial domain basis (SD basis) and frequency domain basis (FD basis) of DFT (Discrete Fourier Transform) to obtain the main characteristic information of the channel. The information obtained after preprocessing is independent of the transmit antenna port configuration and the number of frequency domain units, which is beneficial to reducing the number of CSI generation models that need to be trained.

[0041] However, on the one hand, although the dimension of the information obtained by preprocessing on the SD basis and FD basis of DFT is not related to the configuration of the transmitting antenna port, it is related to the number of receiving antenna (terminal side antenna) ports. For different numbers of receiving antenna ports, different CSI generation partial models still need to be trained, and there is still the problem of needing to train too many CSI generation partial models.

[0042] On the other hand, since both SD basis and FD basis are DFT basis (discrete Fourier transform basis vectors), the information obtained after SD basis and FD basis preprocessing has the characteristics of the DFT codebook. However, due to the quantization characteristics of the DFT codebook, there will be a lot of information loss when the CSI recovery model recovers the quantized data, which affects the network equipment's determination of the downlink channel status.

[0043] Figure 2 is a schematic flow chart illustrating a method for transmitting information according to an embodiment of the present disclosure. The information transmitting method illustrated in this embodiment can be executed by a terminal, including but not limited to a mobile phone, tablet computer, wearable device, sensor, IoT device, and other communication devices. The terminal can communicate with network devices, including but not limited to network devices in any generation of communication systems, such as 4G, 5G, and 6G, such as base stations and core networks.

[0044] As shown in FIG2 , the information sending method may include the following steps:

[0045] In step S201, estimated full channel information is preprocessed according to a first number of spatial basis vectors and a second number of frequency basis vectors to obtain effective channel information;

[0046] In step S202, the eigenvector of the effective channel information is calculated;

[0047] In step S203, the feature vector is input into a first artificial intelligence and / or machine learning model to obtain CSI-related information, and the CSI-related information is sent to a network device.

[0048] In one embodiment, the terminal may receive a downlink state information reference signal (CSI-RS, Channel State Information Reference Signal) sent by a network device, and determine estimated downlink full channel information based on the CSI-RS, wherein the estimated downlink full channel information includes at least spatial domain channel information and frequency domain channel information.

[0049] Further, the spatial domain basis vectors and the frequency domain basis vectors may be determined based on the estimated full channel information. How to determine the spatial domain basis vectors and the frequency domain basis vectors will be described in subsequent embodiments.

[0050] Next, the estimated full channel information can be preprocessed based on the first number of spatial basis vectors and the second number of frequency domain basis vectors (the preprocessing method can refer to the relevant technology and is not described in detail in this disclosure) to obtain effective channel information, and the dimension of the effective channel information is determined based on the number of receiving antenna ports of the terminal, the first number and the second number.

[0051] For example, if a terminal communicates using dual-polarized antennas, with the first number L and the second number M, and the number of receive antenna ports Nr, then the dimension of the effective channel information is equal to twice the product of Nr and L and M, or Nr × 2LM. This shows that the dimension of the effective channel information is related to the number of receive antenna ports Nr.

[0052] According to the embodiments of the present disclosure, the eigenvector of the effective channel information can be further calculated, for example, by performing eigenvalue analysis on the effective channel information to obtain the eigenvector, then the dimension of the eigenvector is irrelevant to the number of receiving antenna ports Nr. Thus, by using the eigenvector as the input of the first artificial intelligence and / or machine learning model (such as the above-mentioned CSI generation part model), there is no need to consider different scenarios with different numbers of receiving antenna ports and train different models, which is beneficial to reducing the number of models that need to be trained, thereby reducing the number of models that need to be sent to the terminal and the number of models that need to be saved by the terminal, which is beneficial to saving signaling overhead and saving storage space of the terminal. In all embodiments of the present disclosure, the first number L and the second number M may be the same or different, and this is not limited in the embodiments of the present disclosure.

[0053] FIG3 is a schematic flow chart of another information sending method according to an embodiment of the present disclosure. As shown in FIG3 , the method further includes:

[0054] In step S301, current statistical downlink full channel information is determined based on current estimated downlink full channel information and historical statistical downlink full channel information;

[0055] In step S302, eigenvalue decomposition is performed based on the current statistical downlink full channel information to obtain the first number of spatial domain basis vectors and the second number of frequency domain basis vectors;

[0056] The types of the spatial domain basis vectors and the frequency domain basis vectors are eigenvectors.

[0057] In one embodiment, for example, the current time is t, and the history is for example t-1, which refers to any time point before the current time. The current estimated downlink full channel information can be recorded as H t , the historical statistical downlink full channel information can be recorded as H' t-1 , the current statistical downlink full channel information can be recorded as H' t For example, a filtering algorithm with α<1 can be used to calculate H' t , for example H' t =αH t +(1-α)H' t-1 .

[0058] Furthermore, eigenvalue decomposition is performed based on the statistical downlink full channel information at the current moment. For example, H' can be determined first. t The transposed matrix (H' t ) H , then (H' t ) H H' t Perform eigenvalue decomposition to obtain multiple eigenvectors, and select the eigenvectors with the largest eigenvalues ​​as the first number of spatial basis vectors; similarly, H' t (H' t ) H Perform eigenvalue decomposition to obtain multiple eigenvectors, and select a second number of eigenvectors with the largest eigenvalues ​​as the second number of frequency domain basis vectors. The first number of spatial basis vectors and the second number of frequency domain basis vectors obtained in this way are both eigenvectors.

[0059] On this basis, the terminal can preprocess the current statistical downlink full channel information through the first number of spatial basis vectors and the second number of frequency domain basis vectors to obtain effective channel information The dimension is Nr×2LM. Further eigenvalue decomposition is performed, for example or Perform eigenvalue decomposition to obtain multiple eigenvectors, and the dimensions of the obtained eigenvectors are independent of the number Nr of receiving antenna ports.

[0060] In addition, since the first number of spatial basis vectors and the second number of frequency domain basis vectors in this embodiment are both eigenvectors rather than DFT basis vectors, the effective channel information obtained after preprocessing of the spatial basis vectors and the frequency domain basis vectors does not have the characteristics of the DFT codebook. Therefore, due to the quantization characteristics of the DFT codebook, there will not be excessive information loss in the CSI recovery model when recovering the quantized data, which is beneficial to ensure that the network equipment accurately recovers the information reported by the terminal, so as to accurately determine the status of the downlink channel.

[0061] FIG4 is a schematic flow chart of another information sending method according to an embodiment of the present disclosure. As shown in FIG4 , the method further includes:

[0062] In step S401, the first number of spatial basis vectors and the second number of frequency domain basis vectors are calculated according to the estimated downlink full channel information, wherein the types of the spatial basis vectors and the frequency domain basis vectors are discrete Fourier transform DFT basis vectors (DFT basis).

[0063] In one embodiment, the terminal can calculate the first number of spatial basis vectors and the second number of frequency domain basis vectors of the DFT basis vector type according to the estimated downlink full channel information, and then pre-process the statistical downlink full channel information at the current moment by the first number of spatial basis vectors and the second number of frequency domain basis vectors to obtain the effective channel information. The dimension is Nr×2LM. Further eigenvalue decomposition is performed, for example or Perform eigenvalue decomposition to obtain multiple eigenvectors, and the dimensions of the obtained eigenvectors are independent of the number Nr of receiving antenna ports.

[0064] FIG5 is a schematic flow chart of another information sending method according to an embodiment of the present disclosure. As shown in FIG5 , the method further includes:

[0065] In step S501, the spatial domain basis vectors and the frequency domain basis vectors are reported to the network device.

[0066] In one embodiment, the terminal can quantize the CSI-related information and then report it, and the network device can input the quantized CSI-related information into a second artificial intelligence and / or machine learning model (such as a CSI recovery partial model) to obtain the recovery information of the estimated full channel information, and then construct the estimated full channel information and / or channel information feature vector and / or precoding for downlink data transmission based on the recovery information, the frequency domain basis vector and the spatial domain basis vector.

[0067] That is, the network device constructs (which can be called recovery) an estimate of the full channel information, which needs to be implemented based on the spatial domain basis vectors and the frequency domain basis vectors. Therefore, the terminal can report the spatial domain basis vectors and the frequency domain basis vectors to the network device for use by the network device.

[0068] In one embodiment, reporting the spatial domain basis vectors and the frequency domain basis vectors to the network device includes:

[0069] reporting the spatial domain basis vectors and the frequency domain basis vectors to the network device respectively; and / or

[0070] The spatial domain basis vectors and the frequency domain basis vectors are jointly reported to the network device.

[0071] There are two ways to report spatial basis vectors and frequency basis vectors:

[0072] One method is to report the spatial domain basis vector and the frequency domain basis vector separately, for example, reporting the spatial domain basis vector and the frequency domain basis vector as two independent values, or reporting the indexes corresponding to the spatial domain basis vector and the frequency domain basis vector respectively.

[0073] Another method is to report the spatial domain basis vector and the frequency domain basis vector jointly. For example, the spatial domain basis vector and the frequency domain basis vector pair can be reported. For example, they can be reported in the form of tensor product. For example, if the spatial domain basis vector is f and the frequency domain basis vector is s, then Represents a tensor product.

[0074] In one embodiment, reporting the spatial domain basis vectors and the frequency domain basis vectors to the network device includes:

[0075] When the type of the spatial basis vector is a eigenvector, quantizing the real part and the imaginary part of the spatial basis vector respectively and reporting them to the network device; and / or

[0076] When the type of the frequency domain basis vector is a eigenvector, the real part and the imaginary part of the frequency domain basis vector are quantized respectively and then reported to the network device.

[0077] Since the spatial basis vector type is an eigenvector, the spatial basis vector can be represented in complex form. The complex number contains a real part and an imaginary part. To facilitate quantization, the real part and the imaginary part can be quantized separately before being reported to the network device. Similarly, when the frequency domain basis vector type is an eigenvector, the frequency domain basis vector can be represented in complex form. The complex number contains a real part and an imaginary part. To facilitate quantization, the real part and the imaginary part can be quantized separately before being reported to the network device.

[0078] In one embodiment, reporting the spatial domain basis vectors and the frequency domain basis vectors to the network device includes:

[0079] Representing the spatial domain basis vectors and / or the frequency domain basis vectors as a linear combination of a plurality of orthogonal basis vectors and a plurality of coefficients corresponding to the orthogonal basis vectors;

[0080] The coefficients corresponding to the multiple orthogonal basis vectors are reported to the network device.

[0081] For spatial domain basis vectors and / or frequency domain basis vectors, they can be represented as a linear combination of multiple orthogonal basis vectors and multiple orthogonal basis vector corresponding coefficients, wherein the terminal and the network device can know the way of representing the linear combination, that is, the network device can determine how the terminal represents the spatial domain basis vectors and / or frequency domain basis vectors as a linear combination of multiple orthogonal basis vectors and multiple orthogonal basis vector corresponding coefficients. Since the orthogonal basis vectors are relatively fixed in the linear combination, it is only necessary to report the multiple orthogonal basis vector corresponding coefficients to the network device.

[0082] It should be noted that the processing method for the spatial domain basis vectors and the frequency domain basis vectors during the reporting process is not limited to the above-mentioned embodiments. For example, the amplitude value and phase value of each element in the spatial domain basis vector can also be determined, and then the amplitude value and phase value of each element in the spatial domain basis vector are quantized separately and reported to the network device; accordingly, the amplitude value and phase value of each element in the frequency domain basis vector can be determined, and then the amplitude value and phase value of each element in the frequency domain basis vector are quantized separately and reported to the network device.

[0083] In one embodiment, the effective channel information includes at least one of the following:

[0084] one or more feature vectors of the plurality of data transmission layers obtained by preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency basis vectors;

[0085] After preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency domain basis vectors, channel information of one or more receiving antenna ports among the channel information of multiple receiving antenna ports is obtained.

[0086] The effective channel information obtained by preprocessing the estimated full channel information through a first number of spatial domain basis vectors and a second number of frequency domain basis vectors may be the feature vectors of one or more data transmission layers among the feature vectors of multiple data transmission layers, such as the feature vectors of the rank=v layer, or may be the channel information of one or more receiving antenna ports among the channel information of multiple receiving antenna ports, such as the channel information of the rth antenna port among Nr antenna ports, or the channel information of all Nr antenna ports.

[0087] In one embodiment, the number of dimensions of the effective channel information is determined based on at least one of the following:

[0088] the first quantity and the second quantity;

[0089] The number of pairs of the spatial domain basis vectors and the frequency domain basis vectors;

[0090] Number of receive antenna ports.

[0091] For example, it may be twice the product of the number of receiving antenna ports and the first number and the second number.

[0092] In one embodiment, the first number and the second number are determined based on at least one of the following:

[0093] the first number of the network device configurations;

[0094] the second number of the network device configurations;

[0095] The sum of the first number and the second number of the network device configurations;

[0096] The product of the first number and the second number of the network device configurations;

[0097] Downlink channel information.

[0098] The network device can directly configure the first quantity and the second quantity for the terminal; it can also configure the sum or product of the first quantity and the second quantity for the terminal. The terminal can autonomously determine the first quantity or the second quantity, and then determine the second quantity or the first quantity based on the sum or product of the first quantity and the second quantity configured by the network device. For example, the network device is configured with the sum of the first quantity and the second quantity. After the terminal autonomously determines the first quantity, it can be determined that the second quantity is equal to the sum configured by the network device minus the first quantity. The terminal can also determine the first quantity and the second quantity by monitoring the downlink channel information. For example, if the downlink channel quality is determined to be relatively good based on the downlink channel information, then the first quantity and / or the second quantity can be relatively small. For example, if the downlink channel quality is determined to be relatively poor based on the downlink channel information, then the first quantity and / or the second quantity can be relatively large.

[0099] Those skilled in the art will appreciate that the aforementioned multiple embodiments executed by the terminal may be implemented individually or combined in any manner, and the embodiments of the present disclosure are not limited thereto.

[0100] Figure 6 is a schematic flow chart illustrating a method for receiving information according to an embodiment of the present disclosure. The information receiving method illustrated in this embodiment can be executed by a network device that can communicate with a terminal, including but not limited to base stations in communication systems such as 4G base stations, 5G base stations, and 6G base stations, and the terminal including but not limited to mobile phones, tablet computers, wearable devices, sensors, IoT devices, and other communication devices.

[0101] As shown in FIG6 , the information receiving method may include the following steps:

[0102] In step S601, the receiving terminal sends CSI-related information according to the method described in any one of the above embodiments.

[0103] In one embodiment, the method further comprises:

[0104] Inputting the CSI-related information into a second artificial intelligence and / or machine learning model to obtain recovered information of the estimated full channel information;

[0105] The estimated full channel information and / or channel information eigenvectors and / or precoding for downlink data transmission are constructed based on the recovered information, the frequency domain basis vectors and the spatial domain basis vectors.

[0106] For example, if a terminal communicates via dual-polarized antennas, with a first number L = 4, a second number M = 4, and the number of receive antenna ports Nr = 2, the dimension of the effective channel information is equal to twice the basis of Nr and L and M, or 64. Furthermore, the eigenvector of the effective channel information can be calculated and input into the CSI generation model for compression to generate 20 codewords. Each codeword can be quantized to 2 bits, resulting in a 40-bit data stream that is sent to the network device.

[0107] The network device can input the received 40-bit data stream into a second artificial intelligence and / or machine learning model, such as a CSI recovery model, to recover information approximating the eigenvector as recovered information. The device then constructs estimated full channel information based on the frequency-domain basis vectors and spatial-domain basis vectors to determine the downlink channel condition. Furthermore, the device can construct channel information eigenvectors and precoding for downlink data transmission.

[0108] In one embodiment, the spatial domain basis vectors and the frequency domain basis vectors include at least one of the following:

[0109] The spatial domain basis vectors and frequency domain basis vectors reported by the terminal when sending the CSI related information;

[0110] The spatial domain basis vectors and frequency domain basis vectors reported historically by the terminal.

[0111] When sending CSI-related information (quantized data stream), the terminal can also report the spatial domain basis vectors and frequency domain basis vectors to the network device. Then, when the network device constructs the estimated full channel information, it can use the spatial domain basis vectors and frequency domain basis vectors reported by the terminal.

[0112] However, if the terminal does not report the spatial domain basis vectors and frequency domain basis vectors to the network device when sending CSI-related information (quantized data stream), the network device can use the spatial domain basis vectors and frequency domain basis vectors reported by the terminal in the past when constructing the estimated full channel information.

[0113] Corresponding to the aforementioned embodiments of the information sending method and the information receiving method, the present disclosure also provides embodiments of an information sending device and an information receiving device.

[0114] Figure 7 is a schematic block diagram of an information transmission device according to an embodiment of the present disclosure. The information transmission device shown in this embodiment can be a terminal, or a device composed of modules within a terminal. The terminal includes, but is not limited to, a mobile phone, tablet computer, wearable device, sensor, IoT device, and other communication devices. The terminal can communicate with network devices, including, but not limited to, network devices in 4G, 5G, and 6G communication systems, such as base stations and core networks.

[0115] As shown in FIG7 , the information sending device includes:

[0116] The processing module 701 is configured to preprocess the estimated full channel information according to the first number of spatial basis vectors and the second number of frequency basis vectors to obtain effective channel information; calculate a feature vector of the effective channel information; and input the feature vector into a first artificial intelligence and / or machine learning model to obtain CSI-related information;

[0117] The sending module 702 is configured to send the CSI-related information to a network device.

[0118] In one embodiment, the processing module is further configured to determine the current statistical downlink full channel information based on the current estimated downlink full channel information and the historical statistical downlink full channel information; perform eigenvalue decomposition based on the current statistical downlink full channel information to obtain the first number of spatial domain basis vectors and the second number of frequency domain basis vectors; wherein the type of the spatial domain basis vectors and the frequency domain basis vectors is eigenvector.

[0119] In one embodiment, the processing module is further configured to calculate the first number of spatial domain basis vectors and the second number of frequency domain basis vectors based on the estimated downlink full channel information, wherein the types of the spatial domain basis vectors and the frequency domain basis vectors are discrete Fourier transform DFT basis vectors.

[0120] In one embodiment, the sending module is further configured to report the spatial domain basis vectors and the frequency domain basis vectors to the network device.

[0121] In one embodiment, the sending module is configured to report the spatial domain basis vectors and the frequency domain basis vectors to the network device separately; and / or report the spatial domain basis vectors and the frequency domain basis vectors jointly to the network device.

[0122] In one embodiment, the sending module is configured to quantize the real part and the imaginary part of the spatial domain basis vector separately and report them to the network device when the type of the spatial domain basis vector is a eigenvector; and / or to quantize the real part and the imaginary part of the frequency domain basis vector separately and report them to the network device when the type of the frequency domain basis vector is a eigenvector.

[0123] In one embodiment, the processing module is configured to represent the spatial domain basis vectors and / or the frequency domain basis vectors as a linear combination of multiple orthogonal basis vectors and multiple coefficients corresponding to the orthogonal basis vectors; the sending module is configured to report the coefficients corresponding to the multiple orthogonal basis vectors to the network device.

[0124] In one embodiment, the effective channel information includes at least one of the following:

[0125] one or more feature vectors of the plurality of data transmission layers obtained by preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency basis vectors;

[0126] After preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency domain basis vectors, channel information of one or more receiving antenna ports among the channel information of multiple receiving antenna ports is obtained.

[0127] In one embodiment, the number of dimensions of the effective channel information is determined based on at least one of the following: the first number and the second number; the number of pairs of the spatial domain basis vectors and the frequency domain basis vectors; and the number of receiving antenna ports.

[0128] In one embodiment, the first number and the second number are determined based on at least one of the following: the first number configured by the network device; the second number configured by the network device; the sum of the first number and the second number configured by the network device; the product of the first number and the second number configured by the network device; downlink channel information.

[0129] Figure 8 is a schematic block diagram of an information receiving device according to an embodiment of the present disclosure. The information receiving device shown in this embodiment can be a network device, or a device composed of modules within a network device, which can communicate with a terminal. The terminal includes, but is not limited to, mobile phones, tablets, wearable devices, sensors, IoT devices, and other communication devices. The network device includes, but is not limited to, network devices in 4G, 5G, and 6G communication systems, such as base stations and core networks.

[0130] As shown in FIG8 , the information receiving device includes:

[0131] The receiving module 801 is configured to receive CSI-related information sent by a terminal according to the apparatus described in any of the above embodiments.

[0132] In one embodiment, the device also includes: a processing module, configured to input the CSI-related information into a second artificial intelligence and / or machine learning model to obtain the recovery information of the estimated full channel information; based on the recovery information, the frequency domain basis vector and the spatial domain basis vector, construct the estimated full channel information, and / or channel information feature vector, and / or precoding for downlink data transmission.

[0133] In one embodiment, the spatial domain basis vectors and the frequency domain basis vectors include at least one of the following: the spatial domain basis vectors and the frequency domain basis vectors reported by the terminal when sending the CSI-related information; the spatial domain basis vectors and the frequency domain basis vectors reported by the terminal in history.

[0134] Regarding the apparatus in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the relevant methods and will not be elaborated on here.

[0135] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0136] An embodiment of the present disclosure further proposes an information sending and receiving system, comprising a terminal and a network side device, wherein the terminal is configured to implement the information sending method described in any of the above embodiments, and the network device is configured to implement the information receiving method described in any of the above embodiments.

[0137] An embodiment of the present disclosure further proposes a communication device, comprising: a processor; and a memory for storing a computer program; wherein, when the computer program is executed by the processor, the information sending method described in any of the above embodiments is implemented.

[0138] An embodiment of the present disclosure further proposes a communication device, comprising: a processor; and a memory for storing a computer program; wherein, when the computer program is executed by the processor, the information receiving method described in any of the above embodiments is implemented.

[0139] An embodiment of the present disclosure further provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the information sending method described in any of the above embodiments is implemented.

[0140] An embodiment of the present disclosure further provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the information receiving method described in any of the above embodiments is implemented.

[0141] As shown in Figure 9, Figure 9 is a schematic block diagram of an apparatus 900 for receiving information according to an embodiment of the present disclosure. Apparatus 900 can be provided as a base station. Referring to Figure 9, apparatus 900 includes a processing component 922, a wireless transmit / receive component 924, an antenna component 926, and a signal processing portion specific to a wireless interface. Processing component 922 may further include one or more processors. One of the processors in processing component 922 can be configured to implement the information receiving method described in any of the above embodiments.

[0142] Figure 10 is a schematic block diagram of an apparatus 1000 for sending information according to an embodiment of the present disclosure. For example, the apparatus 1000 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0143] 10 , apparatus 1000 may include one or more of the following components: a processing component 1002 , a memory 1004 , a power component 1006 , a multimedia component 1008 , an audio component 1010 , an input / output (I / O) interface 1012 , a sensor component 1014 , and a communication component 1016 .

[0144] The processing component 1002 generally controls the overall operation of the device 1000, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 1002 may include one or more processors 1020 to execute instructions to perform all or part of the steps of the above-described information transmission method. In addition, the processing component 1002 may include one or more modules to facilitate interaction between the processing component 1002 and other components. For example, the processing component 1002 may include a multimedia module to facilitate interaction between the multimedia component 1008 and the processing component 1002.

[0145] The memory 1004 is configured to store various types of data to support operations on the device 1000. Examples of such data include instructions for any application or method operating on the device 1000, contact data, phone book data, messages, pictures, videos, etc. The memory 1004 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0146] The power supply component 1006 provides power to the various components of the device 1000. The power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 1000.

[0147] The multimedia component 1008 includes a screen that provides an output interface between the device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1008 includes a front camera and / or a rear camera. When the device 1000 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0148] The audio component 1010 is configured to output and / or input audio signals. For example, the audio component 1010 includes a microphone (MIC) that is configured to receive external audio signals when the device 1000 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 1004 or transmitted via the communication component 1016. In some embodiments, the audio component 1010 also includes a speaker for outputting audio signals.

[0149] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, volume buttons, a start button, and a lock button.

[0150] The sensor assembly 1014 includes one or more sensors for providing various aspects of the status assessment of the device 1000. For example, the sensor assembly 1014 can detect the open / closed state of the device 1000, the relative positioning of components, such as the display and keypad of the device 1000. The sensor assembly 1014 can also detect changes in the position of the device 1000 or a component of the device 1000, the presence or absence of user contact with the device 1000, the orientation or acceleration / deceleration of the device 1000, and changes in the temperature of the device 1000. The sensor assembly 1014 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 1014 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 can also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0151] The communication component 1016 is configured to facilitate wired or wireless communication between the device 1000 and other devices. The device 1000 can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G LTE, 5G NR, or a combination thereof. In an exemplary embodiment, the communication component 1016 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0152] In an exemplary embodiment, the apparatus 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned information sending method.

[0153] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is further provided, such as a memory 1004 including instructions. The instructions can be executed by the processor 1020 of the apparatus 1000 to perform the above-mentioned information transmission method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0154] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0155] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0156] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0157] The above is a detailed introduction to the methods and devices provided in the embodiments of the present disclosure. Specific examples are used herein to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the methods and core ideas of the present disclosure. At the same time, for those skilled in the art, according to the ideas of the present disclosure, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present disclosure.

Claims

1. A method for sending information, characterized in that: Executed by a terminal, the method includes: Preprocessing the estimated full channel information according to the first number of spatial basis vectors and the second number of frequency basis vectors to obtain effective channel information; Calculating a characteristic vector of the effective channel information; The feature vector is input into a first artificial intelligence and / or machine learning model to obtain CSI-related information, and the CSI-related information is sent to a network device.

2. The method according to claim 1, characterized in that The method further comprises: Determining current statistical downlink full channel information based on the current estimated downlink full channel information and historical statistical downlink full channel information; Performing eigenvalue decomposition according to the current statistical downlink full channel information to obtain the first number of spatial domain basis vectors and the second number of frequency domain basis vectors; The types of the spatial domain basis vectors and the frequency domain basis vectors are eigenvectors.

3. The method according to claim 1, characterized in that The method further comprises: The first number of spatial domain basis vectors and the second number of frequency domain basis vectors are calculated according to the estimated downlink full channel information, wherein the types of the spatial domain basis vectors and the frequency domain basis vectors are discrete Fourier transform DFT basis vectors.

4. The method according to claim 1, wherein The method further comprises: The spatial domain basis vectors and the frequency domain basis vectors are reported to the network device.

5. The method according to claim 4, characterized in that Reporting the spatial domain basis vector and the frequency domain basis vector to the network device includes: reporting the spatial domain basis vectors and the frequency domain basis vectors to the network device respectively; and / or The spatial domain basis vectors and the frequency domain basis vectors are jointly reported to the network device.

6. The method according to claim 4, characterized in that Reporting the spatial domain basis vector and the frequency domain basis vector to the network device includes: When the type of the spatial basis vector is a eigenvector, quantizing the real part and the imaginary part of the spatial basis vector respectively and reporting them to the network device; and / or When the type of the frequency domain basis vector is a eigenvector, the real part and the imaginary part of the frequency domain basis vector are quantized respectively and then reported to the network device.

7. The method according to claim 4, characterized in that Reporting the spatial domain basis vector and the frequency domain basis vector to the network device includes: Representing the spatial domain basis vectors and / or the frequency domain basis vectors as a linear combination of a plurality of orthogonal basis vectors and a plurality of coefficients corresponding to the orthogonal basis vectors; The coefficients corresponding to the multiple orthogonal basis vectors are reported to the network device.

8. The method according to any one of claims 1 to 7, characterized in that The effective channel information includes at least one of the following: one or more feature vectors of the plurality of data transmission layers obtained by preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency basis vectors; After preprocessing the estimated full channel information using the first number of spatial basis vectors and the second number of frequency domain basis vectors, channel information of one or more receiving antenna ports among the channel information of multiple receiving antenna ports is obtained.

9. The method according to any one of claims 1 to 7, characterized in that The number of dimensions of the effective channel information is determined based on at least one of the following: the first quantity and the second quantity; The number of pairs of the spatial domain basis vectors and the frequency domain basis vectors; Number of receive antenna ports.

10. The method according to any one of claims 1 to 7, characterized in that The first number and the second number are determined based on at least one of the following: the first number of the network device configurations; the second number of the network device configurations; The sum of the first number and the second number of the network device configurations; The product of the first number and the second number of the network device configurations; Downlink channel information.

11. A method for receiving information, characterized in that: Executed by a network device, the method includes: A receiving terminal receives CSI-related information sent by the method according to any one of claims 1 to 10.

12. The method according to claim 11, characterized in that The method further comprises: Inputting the CSI-related information into a second artificial intelligence and / or machine learning model to obtain recovered information of the estimated full channel information; The estimated full channel information and / or channel information eigenvectors and / or precoding for downlink data transmission are constructed based on the recovered information, the frequency domain basis vectors and the spatial domain basis vectors.

13. The method according to claim 12, characterized in that The spatial domain basis vectors and the frequency domain basis vectors include at least one of the following: The spatial domain basis vectors and frequency domain basis vectors reported by the terminal when sending the CSI related information; The spatial domain basis vectors and frequency domain basis vectors reported historically by the terminal.

14. An information sending device, characterized in that: The device comprises: a processing module configured to preprocess the estimated full channel information based on a first number of spatial basis vectors and a second number of frequency basis vectors to obtain effective channel information; calculate a feature vector of the effective channel information; and input the feature vector into a first artificial intelligence and / or machine learning model to obtain CSI-related information; The sending module is configured to send the CSI related information to the network device.

15. An information receiving device, characterized in that: The device comprises: The receiving module is configured to receive CSI-related information sent by the terminal according to any one of the methods according to claims 1 to 10.

16. An information sending and receiving system, characterized in that: The invention comprises a terminal and a network side device, wherein the terminal is configured to implement the information sending method according to any one of claims 1 to 10, and the network side device is configured to implement the information receiving method according to any one of claims 11 to 13.

17. A communication device, characterized in that: include: processor; memory for storing computer programs; When the computer program is executed by a processor, the information sending method according to any one of claims 1 to 10 is implemented.

18. A communication device, characterized in that: include: processor; memory for storing computer programs; When the computer program is executed by a processor, the information receiving method according to any one of claims 11 to 13 is implemented.

19. A computer-readable storage medium for storing a computer program, characterized in that: When the computer program is executed by a processor, the information transmission method according to any one of claims 1 to 10 is implemented.

20. A computer-readable storage medium for storing a computer program, characterized in that: When the computer program is executed by a processor, the information receiving method according to any one of claims 11 to 13 is implemented.

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