A method and system for obtaining channel state information

Abstract

The embodiments of the present application provide a method and system for obtaining channel state information. The method includes: The base station calculates a singular ratio threshold according to the information sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal; The terminal performs singular value decomposition on the channel matrix to obtain a diagonal matrix containing singular values; If the terminal determines that the ratio of the maximum singular value of the diagonal matrix is greater than or equal to the singular ratio threshold, it determines that the diagonal matrix is a characteristic diagonal matrix; If the counted number of characteristic diagonal matrices is greater than or equal to the first set value, it generates the first channel state information and feeds it back to the base station; If the counted number is less than the second set value, it generates the second channel state information and feeds it back to the base station; If the counted number is between the first and second set values, it generates the third channel state information and feeds it back to the base station. The embodiments of the present application can solve the problem of poor flexibility in the base station obtaining channel state information and improve the service quality of the network.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to a method and a system for obtaining channel state information. Background Art

[0002] Future mobile communication systems will meet the diverse service needs of people in various areas such as residence, work, leisure, and transportation. Even in scenarios with characteristics of ultra-high traffic density, ultra-high connection density, and ultra-high mobility, such as dense residential areas, offices, stadiums, open-air gatherings, subways, expressways, high-speed rails, and wide-area coverage, it can also provide users with extreme service experiences such as ultra-high-definition videos, virtual reality, augmented reality, cloud desktops, and online games. At the same time, future mobile communication systems will also penetrate into the Internet of Things and various industrial fields, deeply integrate with industrial facilities, medical instruments, transportation tools, etc., and effectively meet the diverse service needs of vertical industries such as industry, medical care, and transportation, realizing true "Internet of Everything".

[0003] The application scenarios of future mobile communication systems can be divided into two categories, namely mobile broadband (MBB) and the Internet of Things (IoT). Among them, the main technical requirement for mobile broadband access is high capacity to provide high data rates to meet the growing demand for data services. The Internet of Things is mainly driven by the demand for machine type communication (MTC), and can be further divided into two types, including low-rate massive machine communication (MMC) and low-latency and high-reliability machine communication. Among them, for low-rate massive machine communication, a large number of nodes access at low rates, the transmitted data packets are usually small, and the interval time will be relatively long. The cost and power consumption of such nodes are usually very low; for low-latency and high-reliability machine communication, it is mainly for machine communication with relatively high requirements for real-time performance and reliability, such as real-time alarms and real-time monitoring.

[0004] In a mobile communication system, feedback information related to the wireless channel is the basis for effective data transmission. How to obtain high-quality channel state information is an urgent problem to be solved at present. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method and a system for obtaining channel state information, which can solve the problem of poor flexibility in obtaining channel state information by a base station and improve the service quality of the network.

[0006] One aspect of the embodiments of the present application provides a method for obtaining channel state information, including:

[0007] The base station calculates a singular ratio threshold based on the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal;

[0008] The terminal measures the N groups of frequency-domain channel state information reference signals to obtain the channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain the left singular matrix, diagonal matrix containing singular values, and right singular matrix corresponding to each frequency-domain channel;

[0009] For each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values. If the calculated ratio is greater than or equal to the singular ratio threshold, the diagonal matrix is determined as the eigen diagonal matrix; and

[0010] count the number of eigen diagonal matrices; if the counted number is greater than or equal to the first set value, generate first channel state information based on the first column of each right singular matrix and feedback it to the base station; if the counted number is less than the second set value, generate second channel state information based on the time-domain channel vector obtained by Fourier transform of the channel matrices of N frequency-domain channels and feedback it to the base station; if the counted number is between the first and second set values, generate third channel state information based on the first two columns of each right singular matrix and feedback it to the base station.

[0011] Preferably, the base station calculates the singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, specifically including:

[0012] According to the location information, determine whether the current location of the terminal is the location where the base station has previously performed a detailed channel analysis; according to the judgment result, determine the first threshold;

[0013] According to the power information, determine the second threshold;

[0014] According to the sounding reference signal, determine the downlink channel state information, and use the statistical reciprocity of the uplink and downlink channels to determine the uplink channel state information, and then determine the change rate of the uplink channel; according to the change rate of the uplink channel, determine the third threshold;

[0015] Calculate the singular ratio threshold by comprehensively considering the first, second, and third thresholds.

[0016] Preferably, the base station calculates the singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, specifically including:

[0017] According to the location information, determine whether the current location of the terminal is the location where the base station has previously performed a detailed channel analysis; according to the judgment result, determine the first threshold;

[0018] Determine a second threshold according to the power information;

[0019] Determine the downlink channel state information according to the detection reference signal, determine the uplink channel state information by using the statistical reciprocity of the uplink and downlink channels, and further determine the change rate of the uplink channel; determine a third threshold according to the change rate of the uplink channel;

[0020] Calculate a singular ratio threshold by synthesizing the first, second, and third thresholds.

[0021] Preferably, the first encoding neural network includes: 1 convolutional layer, 1 pooling layer, and 1 fully connected layer; and

[0022] The first decoding neural network includes: 3 convolutional layers, 2 pooling layers, and 1 fully connected layer.

[0023] Preferably, generating the second channel state information according to the time-domain channel vector obtained by Fourier transform of the channel matrix of N frequency-domain channels specifically includes:

[0024] Process the time-domain channel vector by using a second encoding neural network based on an autoencoder structure to generate the second channel state information; and the method further includes:

[0025] After receiving the second channel state information, the base station decodes the second channel state information by using a second decoding neural network based on an autoencoder structure to obtain the time-domain channel vector.

[0026] Preferably, the second encoding neural network includes: 2 convolutional layers, 2 pooling layers, and 1 fully connected layer; and

[0027] The second decoding neural network includes: 6 convolutional layers, 5 pooling layers, and 1 fully connected layer.

[0028] Preferably, generating the third channel state information according to the first two columns of each right singular matrix specifically includes:

[0029] Obtain the first two columns of each right singular matrix, project each obtained column vector onto L mutually orthogonal basis vectors to obtain 2N×L projection values, and perform quantization processing on the 2N×L projection values to obtain the third channel state information; where the value of L is an integer greater than or equal to T / 4, and T is the number of transmit antennas of the base station; and the method further includes:

[0030] After receiving the third channel state information, the base station uses L mutually orthogonal basis vectors to obtain the first two columns of N right singular matrices according to the 2N×L projection values.

[0031] Preferably, the first set value is specifically N / 2, and the second set value is specifically N / 4.

[0032] One aspect of the embodiments of the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-described channel state information acquisition method are implemented.

[0033] One aspect of the embodiments of the present application further provides a channel state information acquisition system, including: a base station and a terminal; wherein

[0034] The base station calculates a singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal;

[0035] The terminal measures the N groups of frequency-domain channel state information reference signals to obtain channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain a left singular matrix, a diagonal matrix containing singular values, and a right singular matrix corresponding to each frequency-domain channel;

[0036] For each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values. If the calculated ratio is greater than or equal to the singular ratio threshold, the diagonal matrix is determined as a characteristic diagonal matrix; and

[0037] Counts the number of characteristic diagonal matrices; if the counted number is greater than or equal to a first set value, generates first channel state information according to the first column of each right singular matrix and feeds it back to the base station; if the counted number is less than a second set value, generates second channel state information according to the time-domain channel vector obtained by Fourier transform of the channel matrices of N frequency-domain channels and feeds it back to the base station; if the counted number is between the first and second set values, generates third channel state information according to the first two columns of each right singular matrix and feeds it back to the base station.

[0038] In the technical solution provided by the embodiment of the present application, the base station calculates a singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal; the terminal measures the N groups of frequency-domain channel state information reference signals to obtain channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain a left singular matrix, a diagonal matrix containing singular values, and a right singular matrix corresponding to each frequency-domain channel; for each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values, and if the calculated ratio is greater than or equal to the singular ratio threshold, determines the diagonal matrix as a characteristic diagonal matrix; and counts the number of characteristic diagonal matrices; if the counted number is greater than or equal to a first set value, generates first channel state information according to the first column of each right singular matrix and feeds it back to the base station; if the counted number is less than a second set value, generates second channel state information according to the time-domain channel vector obtained by performing Fourier transform on the channel matrices of N frequency-domain channels and feeds it back to the base station; if the counted number is between the first and second set values, generates third channel state information according to the first two columns of each right singular matrix and feeds it back to the base station.

[0039] In this way, on the one hand, the result obtained by the terminal's singular value decomposition of the channel matrix, that is, the counted number of characteristic diagonal matrices, can be used to analyze the spatial characteristics of the channel. Based on the different spatial characteristics of the channel, corresponding channel processing methods are used to generate channel state information; that is to say, the terminal can feed back channel state information that matches the spatial characteristics of the channel to the base station, providing the base station with a flexible way to obtain channel state information;

[0040] On the other hand, the base station can determine a dynamic threshold according to the location information, power information, and sounding reference signal sent by the terminal and send it to the terminal; based on this dynamic threshold, the terminal can more accurately determine the characteristic diagonal matrix, so as to obtain a more accurate result of the singular value decomposition of the channel matrix. Furthermore, the terminal can more accurately analyze the spatial characteristics of the channel, providing the base station with a more flexible way to obtain channel state information, thus solving the problem of poor flexibility in the base station's acquisition of channel state information and improving the service quality of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematically shows an architecture diagram of a channel state information acquisition system according to an embodiment of the present application;

[0042] Figure 2 Schematically shows a flowchart of a channel state information acquisition method according to an embodiment of the present application;

[0043] Figure 3Schematically shown is a hardware architecture diagram of a computer device suitable for implementing a channel state information acquisition method according to an embodiment of the present application. Detailed implementation manners

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0045] It should be noted that in the embodiments of the present application, the descriptions involving "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions appears to be mutually contradictory or unable to be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present application.

[0046] In the description of the present application, it should be understood that the numerical labels before the steps do not identify the order of execution of the steps, but are only used to facilitate the description of the present application and distinguish each step, and thus cannot be understood as a limitation to the present application.

[0047] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Figure 1 Schematically shown is an architecture of a channel state information acquisition system provided by an embodiment of the present application, including: a base station and a terminal; Figure 2 Schematically shown is a specific flowchart for the channel state information acquisition system to implement the channel state information acquisition method of the embodiment of the present application, including the following steps:

[0049] Step S100: The base station calculates a singular ratio threshold according to the location information, power information and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal.

[0050] Specifically, a terminal with R receiving antennas sends the location information, power information and sounding reference signal of this terminal to a base station with T transmitting antennas;

[0051] The base station calculates a singular ratio threshold VT based on the location information, power information, and sounding reference signal sent by the terminal, and sends the singular ratio threshold VT and N sets of frequency-domain channel state information reference signals to the terminal.

[0052] In an exemplary embodiment, the base station can determine whether the current location of the terminal is a location where the base station has previously performed a detailed channel analysis based on the location information sent by the terminal; according to the determination result, a first threshold is determined. For example, if it is determined that the current location of the terminal is a location where the base station has previously performed a detailed channel analysis, the first threshold can be set to a low threshold value; if it is determined that the current location of the terminal is not a location where the base station has previously performed a detailed channel analysis, the first threshold can be set to a high threshold value. Among them, the low threshold value and the high threshold value can be set by those skilled in the art according to experience or actual situations. For example, the low threshold value is set to 0.3 and the high threshold value is set to 0.7.

[0053] Furthermore, the base station can also determine a second threshold according to the power information of the terminal: if the power information of the terminal indicates that the current current of the terminal is sufficient, the second threshold can be set to a low threshold value; otherwise, the second threshold can be set to a high threshold value.

[0054] In addition, the base station can also determine the downlink channel state information according to the sounding reference signal sent by the terminal, and use the statistical reciprocity of the uplink and downlink channels to determine the uplink channel state information, and then determine the change rate of the uplink channel; according to the change rate of the uplink channel, a third threshold is determined: if it is determined that the change rate of the uplink channel is slow and less than the set rate threshold, the third threshold can be set to a low threshold value; otherwise, the third threshold can be set to a high threshold value.

[0055] The singular ratio threshold is calculated by comprehensively considering the first, second, and third thresholds; for example, the first, second, and third thresholds are averaged to obtain the calculated singular ratio threshold.

[0056] Step S101: The terminal measures the N sets of frequency-domain channel state information reference signals sent by the base station to obtain the channel matrices of N frequency-domain channels.

[0057] Specifically, a terminal with R receiving antennas receives N sets of frequency-domain channel state information reference signals sent by a base station with T transmitting antennas. The terminal measures the frequency-domain channel state information reference signals to obtain the channel matrices of N frequency-domain channels from the base station to the terminal, denoted as FH-i, where the value of i ranges from 1 to N, and FH-i is the channel matrix of the i-th frequency-domain channel, and its dimension is R×T.

[0058] Step S102: The terminal performs singular value decomposition on the channel matrix of each frequency-domain channel to obtain the left singular matrix, diagonal matrix containing singular values, and right singular matrix corresponding to each frequency-domain channel.

[0059] Specifically, the terminal performs singular value decomposition on the channel matrix of each frequency-domain channel to obtain the left singular matrix, the diagonal matrix containing singular values, and the right singular matrix corresponding to each frequency-domain channel; among them, the dimension of the left singular matrix is R×R, the dimension of the diagonal matrix is R×T, and the dimension of the right singular matrix is T×T.

[0060] That is to say, by performing singular value decomposition on the channel matrix of each frequency-domain channel, the terminal can obtain N left singular matrices, N diagonal matrices containing singular values, and N right singular matrices.

[0061] Step S103: For each diagonal matrix, calculate the ratio of the largest singular value in the diagonal matrix to the sum of all singular values. If the calculated ratio is greater than or equal to the singular ratio threshold, then determine the diagonal matrix as the characteristic diagonal matrix.

[0062] Step S104: Count the number of characteristic diagonal matrices; if the counted number is greater than or equal to the first set value, then perform the following step S105; if the counted number is less than the second set value, then perform the following step S107; if the counted number is between the first and second set values, then perform the following step S109.

[0063] Specifically, the result obtained by performing singular value decomposition on the channel matrix through the above steps, that is, the counted number of characteristic diagonal matrices, can be used to analyze the spatial characteristics of the channel. Based on different spatial characteristics of the channel, corresponding channel processing methods are adopted to generate channel state information; that is to say, the terminal can feedback channel state information matching the spatial characteristics of the channel to the base station, providing a flexible way to obtain channel state information, thus solving the problem of poor flexibility in the base station obtaining channel state information and improving the service quality of the network.

[0064] Moreover, the result of the singular value decomposition of the channel matrix is determined according to the dynamic singular ratio threshold calculated by the base station based on the relevant information sent by the terminal, and a more accurate result of the singular value decomposition of the channel matrix can be obtained. That is to say, the characteristic diagonal matrix can be determined more accurately, and then the terminal can analyze the spatial characteristics of the channel more accurately, providing a more flexible way for the base station to obtain channel state information.

[0065] According to simulation and actual measurement analysis, when the counted number of characteristic diagonal matrices is greater than or equal to the first set value, the channel energy from the base station to the terminal is mainly concentrated in specific few directions. Therefore, perform the following step S105, adopt the first channel state processing method to generate the first channel state information; in a preferred embodiment, the first set value can be N / 2;

[0066] If the number of statistically characteristic diagonal matrices is less than the second set value, it indicates that the base station to terminal channel energy is dispersed in relatively many directions. Therefore, perform the following step S107, and use the second channel state processing method to generate the second channel state information; wherein, the first set value is greater than the second set value. In a preferred embodiment, the second set value can be N / 4;

[0067] If the number of statistically characteristic diagonal matrices is between the first and second set values, it indicates that the base station to terminal channel energy is mainly concentrated in specific several directions. Therefore, perform the following step S109, and use the third channel state processing method to generate the third channel state information.

[0068] Step S105: The terminal generates the first channel state information based on the first column of each right singular matrix and feeds it back to the base station;

[0069] In this step, the terminal uses the first channel state processing method: The terminal uses the first encoding neural network based on the autoencoder structure to process the first column of each right singular matrix to generate the first channel state information;

[0070] In an exemplary embodiment, the first encoding neural network includes at least 1 convolutional layer, 1 pooling layer, and 1 fully connected layer. The input of the first encoding neural network is the first column of N right singular matrices. The terminal quantizes the output of the fully connected layer to obtain the first channel state information and feeds the first channel state information back to the base station.

[0071] Since the base station to terminal channel energy is mainly concentrated in specific few directions at this time, according to simulation and actual measurement analysis, the neural network using the autoencoder can well describe this kind of channel.

[0072] More preferably, when the channel signal-to-interference-plus-noise ratio from the base station to the terminal is greater than or equal to 10 dB, the terminal transmits the first channel state information using at least half of the full power. In fact, when the channel signal-to-interference-plus-noise ratio from the base station to the terminal is greater than or equal to 10 dB, it indicates that the downlink channel energy is relatively concentrated and the channel changes relatively slowly, so less power can be used to transmit relevant information.

[0073] Step S106: The base station decodes the first channel state information.

[0074] In this step, after receiving the first channel state information, the base station uses the first decoding neural network based on the autoencoder structure to decode the first channel state information to obtain the first column of each right singular matrix.

[0075] According to the simulation and hardware test results, the optimization results considering the channel recovery accuracy, terminal power consumption, and hardware implementation complexity can be the following exemplary embodiments: The first decoding neural network includes at least 3 convolutional layers, 2 pooling layers, and one fully connected layer; the parameters in the first encoding neural network and the first decoding neural network are jointly trained.

[0076] The computational complexity of the first decoding neural network is 3 times that of the first encoding neural network, so as to make the base station perform as many complex operations as possible and reduce the burden on the terminal.

[0077] Step S107: The terminal generates second channel state information based on the time-domain channel vector obtained by Fourier transform of the channel matrices of N frequency-domain channels, and feeds it back to the base station;

[0078] In this step, the terminal uses a second channel state processing method: uses a second encoding neural network based on the autoencoder structure to process the time-domain channel vector to generate second channel state information.

[0079] In an exemplary embodiment, the second channel state processing method is a second encoding neural network based on the autoencoder structure. The second encoding neural network includes at least 2 convolutional layers, 2 pooling layers, and 1 fully connected layer; the input of the second encoding neural network is R×T time-domain channel vectors. The terminal performs quantization processing on the output of the fully connected layer to obtain the second channel state information, and feeds the second channel state information back to the base station.

[0080] Since the channel energy from the base station to the terminal is dispersed in more directions at this time, according to simulation and actual measurement analysis, the neural network using an autoencoder can well describe this channel.

[0081] More preferably, when the signal-to-interference-plus-noise ratio of the channel from the base station to the terminal is greater than or equal to 10 dB, the terminal transmits the second channel state information at full power. The reason is that the downlink channel energy is relatively dispersed at this time, the channel change is relatively complex, and the possibility of this user for multi-user transmission is relatively large, so it is necessary to improve the probability of successful transmission of this channel state information as much as possible.

[0082] Step S108: The base station decodes the second channel state information.

[0083] In this step, after receiving the second channel state information, the base station uses a second decoding neural network based on the autoencoder structure to decode the second channel state information to obtain the time-domain channel vector.

[0084] According to the simulation and hardware test results, the result of optimization considering channel recovery accuracy, terminal power consumption and hardware implementation complexity can be the following exemplary embodiment: the second decoding neural network includes at least 6 convolutional layers, 5 pooling layers and one fully connected layer; the parameters in the second encoding neural network and the second decoding neural network are jointly trained.

[0085] The amount of computation of the second decoding neural network is 6 times that of the second encoding neural network, so as to allow the base station to perform more complex calculations as much as possible and reduce the burden on the terminal.

[0086] Step S109: the terminal generates third channel state information according to the first two columns of each right singular matrix and feeds it back to the base station;

[0087] In this step, the terminal uses the third channel state processing method to process the first two columns of N right singular matrices: obtain the first two columns of each right singular matrix, project each obtained column vector on L mutually orthogonal basis vectors to obtain 2N×L projection values, quantize the 2N×L projection values to obtain third channel state information, and feed back the third channel state information to the base station.

[0088] The dimension of the time domain channel vector is X*1. Since the dimension X of the time domain channel vector describes the sparsity of the channel, and the sparsity of the channel is related to the characteristics of the downlink channel, after simulation and measurement analysis, and taking into account the complexity of hardware implementation, in an exemplary embodiment, the value of X is an integer greater than or equal to N / 5 and an integer multiple of 2.

[0089] The third channel state information includes description information describing L mutually orthogonal basis vectors, where the value of L is an integer greater than or equal to T / 4. In this way, on the basis of controlling the feedback overhead, as many orthogonal basis vectors as possible can be used to describe the corresponding right singular vectors.

[0090] Since the energy of the base station-to-terminal channel is mainly concentrated in several specific directions, according to simulation and measured analysis, the method of spatial orthogonal projection of the right singular vectors can well describe this channel.

[0091] Preferably, when the signal to noise ratio of the channel from the base station to the terminal is greater than or equal to 10 dB, the terminal uses at least 0.75 times the full power to send the third channel state information. The reason is that the downlink channel energy is relatively dispersed at this time, there is a certain correlation between the time domain channels, and the user is more likely to perform multi-user transmission, so it is necessary to increase the probability of successful transmission of the channel state information as much as possible.

[0092] Step S110: The base station decodes the third channel state information.

[0093] In this step, after receiving the third channel state information, the base station uses L mutually orthogonal basis vectors to obtain the first two columns of the right singular matrix of N based on 2N×L projection values.

[0094] In the technical solution of the present invention, the base station calculates a singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal; the terminal measures the N groups of frequency-domain channel state information reference signals to obtain the channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain the left singular matrix, diagonal matrix containing singular values, and right singular matrix corresponding to each frequency-domain channel; for each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values, and if the calculated ratio is greater than or equal to the singular ratio threshold, determines the diagonal matrix as the characteristic diagonal matrix; and counts the number of characteristic diagonal matrices; if the counted number is greater than or equal to the first set value, generates first channel state information based on the first column of each right singular matrix and feeds it back to the base station; if the counted number is less than the second set value, generates second channel state information based on the time-domain channel vector obtained by performing Fourier transform on the channel matrices of N frequency-domain channels and feeds it back to the base station; if the counted number is between the first and second set values, generates third channel state information based on the first two columns of each right singular matrix and feeds it back to the base station.

[0095] In this way, on the one hand, the result obtained by the terminal's singular value decomposition of the channel matrix, that is, the counted number of characteristic diagonal matrices, can be used to analyze the spatial characteristics of the channel. Based on the different spatial characteristics of the channel, corresponding channel processing methods are used to generate channel state information; that is to say, the terminal can feed back channel state information matching the spatial characteristics of the channel to the base station, providing a flexible way to obtain channel state information, thereby solving the problem of poor flexibility in obtaining channel state information and improving the service quality of the network.

[0096] On the other hand, the base station can determine a dynamic threshold according to the location information, power information, and sounding reference signal sent by the terminal and send it to the terminal; based on this dynamic threshold, the terminal can more accurately determine the characteristic diagonal matrix, so as to obtain a more accurate result of the singular value decomposition of the channel matrix. Furthermore, the terminal can more accurately analyze the spatial characteristics of the channel, providing a more flexible way to obtain channel state information for the base station, thereby solving the problem of poor flexibility in obtaining channel state information by the base station and improving the service quality of the network.

[0097] Figure 3FIG. schematically shows a hardware architecture diagram of a computer device 1300 suitable for implementing a channel state information acquisition method according to an embodiment of the present application. In this embodiment, the computer device 1300 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. For example, it can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of multiple servers), etc. As Figure 3 shown, the computer device 1300 at least includes, but is not limited to: a memory 1310, a processor 1320, and a network interface 1330 that can be communicatively linked to each other through a system bus. Among them:

[0098] The memory 1310 includes at least one type of computer-readable storage medium. The readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 1310 can be an internal storage module of the computer device 1300, such as the hard disk or memory of the computer device 1300. In other embodiments, the memory 1310 can also be an external storage device of the computer device 1300, such as a plug-in hard disk equipped on the computer device 1300, a smart media card (abbreviated as SMC), a secure digital (abbreviated as SD) card, a flash card, etc. Of course, the memory 1310 can also include both the internal storage module and the external storage device of the computer device 1300. In this embodiment, the memory 1310 is generally used to store the operating system and various application software installed on the computer device 1300, such as the program code of the channel state information acquisition method. In addition, the memory 1310 can also be used to temporarily store various data that have been output or will be output.

[0099] In some embodiments, the processor 1320 can be a central processing unit (abbreviated as CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 1320 is generally used to control the overall operation of the computer device 1300, such as performing control and processing related to data interaction or communication with the computer device 1300. In this embodiment, the processor 1320 is used to run the program code stored in the memory 1310 or process data.

[0100] The network interface 1330 may include a wireless network interface or a wired network interface, which is generally used to establish a communication link between the computer device 1300 and other computer devices. For example, the network interface 1330 is used to connect the computer device 1300 to an external terminal through a network, and to establish a data transmission channel and a communication link between the computer device 1300 and the external terminal. The network may be a wireless or wired network such as an enterprise intranet (Intranet), the Internet, the Global System of Mobile communication (GSM for short), Wideband Code Division Multiple Access (WCDMA for short), a 4G network, a 5G network, Bluetooth, Wi-Fi, etc.

[0101] It should be noted that Figure 3 only the computer device having components 1310-1330 is shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0102] In this embodiment, the channel state information acquisition method stored in the memory 1310 may also be divided into one or more program modules and executed by one or more processors (processor 1320 in this embodiment) to complete the embodiments of the present application.

[0103] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the channel state information acquisition method in the embodiments are implemented.

[0104] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a Smart Media Card (SMC for short), a Secure Digital (SD for short) card, a Flash Card, etc. Of course, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device, such as the program code of the channel state information acquisition method in the embodiment. In addition, the computer-readable storage medium can also be used to temporarily store various data that have been output or will be output.

[0105] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the embodiments of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0106] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for obtaining channel state information, characterized in that, Including: The base station calculates a singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N sets of frequency-domain channel state information reference signals to the terminal; The terminal measures the N sets of frequency-domain channel state information reference signals to obtain the channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain the left singular matrix, diagonal matrix containing singular values, and right singular matrix corresponding to each frequency-domain channel; For each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values. If the calculated ratio is greater than or equal to the singular ratio threshold, the diagonal matrix is determined as the characteristic diagonal matrix; And Count the number of characteristic diagonal matrices; if the counted number is greater than or equal to the first set value, generate first channel state information based on the first column of each right singular matrix and feedback it to the base station; if the counted number is less than the second set value, generate second channel state information based on the time-domain channel vector obtained by performing Fourier transform on the channel matrices of N frequency-domain channels and feedback it to the base station; if the counted number is between the first and second set values, generate third channel state information based on the first two columns of each right singular matrix and feedback it to the base station.

2. The method according to claim 1, wherein The base station calculates the singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, specifically including: According to the location information, determine whether the current location of the terminal is the location where the base station has previously performed a detailed channel analysis; according to the judgment result, determine the first threshold; According to the power information, determine the second threshold; According to the sounding reference signal, determine the downlink channel state information, and use the statistical reciprocity of the uplink and downlink channels to determine the uplink channel state information, and then determine the change rate of the uplink channel; according to the change rate of the uplink channel, determine the third threshold; Integrate the first, second, and third thresholds to calculate the singular ratio threshold.

3. The method according to claim 1, wherein The generating of the first channel state information based on the first column of each right singular matrix specifically includes: Using a first encoding neural network based on an autoencoder structure to process the first column of each right singular matrix to generate the first channel state information; and the method further includes: After receiving the first channel state information, the base station uses a first decoding neural network based on an autoencoder structure to decode the first channel state information to obtain the first column of each right singular matrix.

4. The method according to claim 3, characterized in that, The first encoding neural network includes: 1 convolutional layer, 1 pooling layer, and 1 fully connected layer; and The first decoding neural network includes: 3 convolutional layers, 2 pooling layers, and 1 fully connected layer.

5. The method according to claim 1, wherein The generating of the second channel state information based on the time-domain channel vector obtained by performing Fourier transform on the channel matrices of N frequency-domain channels specifically includes: Using a second encoding neural network based on an autoencoder structure to process the time-domain channel vector to generate the second channel state information; and the method further includes: After receiving the second channel state information, the base station uses a second decoding neural network based on an autoencoder structure to decode the second channel state information to obtain the time-domain channel vector.

6. The method according to claim 5, wherein The second encoding neural network includes: 2 convolutional layers, 2 pooling layers, and 1 fully-connected layer; and The second decoding neural network includes: 6 convolutional layers, 5 pooling layers, and 1 fully-connected layer.

7. The method according to claim 1, wherein Generating the third channel state information according to the first two columns of each right singular matrix specifically includes: Obtaining the first two columns of each right singular matrix, projecting each obtained column vector onto L mutually orthogonal basis vectors to obtain 2N×L projection values, and performing quantization processing on the 2N×L projection values to obtain the third channel state information; where the value of L is an integer greater than or equal to T / 4, and T is the number of transmit antennas of the base station; and the method further includes: After receiving the third channel state information, the base station uses L mutually orthogonal basis vectors to obtain the first two columns of N right singular matrices according to the 2N×L projection values.

8. The method according to any one of claims 1-7, characterized in that, The first set value is specifically N / 2, and the second set value is specifically N / 4.

9. A computer device, the computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When executing the computer program, the processor is used to implement the steps of the channel state information acquisition method according to any one of claims 1 to 8.

10. A channel state information acquisition system, characterized in that, Including: A base station and a terminal; Wherein The base station calculates a singular ratio threshold according to the location information, power information, and sounding reference signal sent by the terminal, and returns the singular ratio threshold and N groups of frequency-domain channel state information reference signals to the terminal; The terminal measures the N groups of frequency-domain channel state information reference signals to obtain channel matrices of N frequency-domain channels; and performs singular value decomposition on the channel matrices to obtain the left singular matrix, diagonal matrix containing singular values, and right singular matrix corresponding to each frequency-domain channel; For each diagonal matrix, the terminal calculates the ratio of the largest singular value in the diagonal matrix to the sum of all singular values. If the calculated ratio is greater than or equal to the singular ratio threshold, the diagonal matrix is determined as the eigen diagonal matrix; And Count the number of eigen diagonal matrices; if the counted number is greater than or equal to the first set value, generate first channel state information according to the first column of each right singular matrix and feedback it to the base station; if the counted number is less than the second set value, generate second channel state information according to the time-domain channel vector obtained by Fourier transform of the channel matrices of N frequency-domain channels and feedback it to the base station; if the counted number is between the first and second set values, generate third channel state information according to the first two columns of each right singular matrix and feedback it to the base station.

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