Rank information determination method, apparatus, device, medium, and product

Abstract

The application relates to the technical field of communication, in particular to a rank information determination method, device, equipment, medium and product, the method comprising the following steps: acquiring pre-processing signal-to-noise ratio (SNR) data of a receiving antenna under different channel space parameters and post-processing SNR data corresponding to different transmission layers of the receiving antenna; determining a corresponding first mutual information (MI) according to each pre-processing SNR data; determining a second MI of the transmission layer under different amplitude modulation modes according to the post-processing SNR data corresponding to each transmission layer; and determining target rank information of the receiving antenna according to the first MIs and the second MIs. The first MI and the second MI are two measures, and various factors are considered, so that the target rank information can be determined based on the first MIs and the second MIs, the reliability of the target rank information is improved, and the transmission performance of a multiple-input multiple-output (MIMO) antenna system is improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, device, medium and product for determining rank information. Background Technology

[0002] In multiple-input multiple-output (MIMO) antenna systems with limited feedback, appropriate rank information is crucial for improving the transmission performance of the system. Rank information includes a rank identifier and a precoding matrix identifier. The rank identifier reflects the rank of the channel matrix, i.e., the number of independent data streams that can be transmitted simultaneously (the number of spatial layers). The precoding matrix identifier is used to indicate a specific precoding matrix in the codebook, thereby determining the spatial codeword for each layer.

[0003] Currently, the selection of rank information is based on channel capacity or received power, which results in poor reliability of the selected rank information and fails to improve the transmission performance of multiple-input multiple-output antenna systems. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, device, medium, and product for determining rank information to address the aforementioned technical problems, so as to improve the reliability of the selected rank information.

[0005] Firstly, this application provides a method for determining rank information, including:

[0006] Acquire preprocessed signal-to-noise ratio (SNR) data of the receiving antenna under different channel spatiotemporal parameters, and postprocessed SNR data of the receiving antenna at different transmission layers;

[0007] Based on each preprocessed signal-to-noise ratio data, determine the corresponding first mutual information (MI);

[0008] Based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, determine the second MI of the transmission layer under different amplitude modulation methods;

[0009] The target rank information of the receiving antenna is determined based on each first MI and each second MI.

[0010] In one embodiment, determining the target rank information of the receiving antenna based on each first MI and each second MI includes: determining the candidate precoding matrix identifier (PMI) corresponding to each transmission layer based on each second MI corresponding to each transmission layer; determining the target rank identifier based on each first MI, and using the candidate PMI corresponding to the transmission layer where the target rank identifier is located as the target PMI.

[0011] In one embodiment, determining the candidate precoding matrix identifier (PMI) corresponding to each transport layer based on each second MI corresponding to each transport layer includes: taking the PMI corresponding to the largest second MI among each second MI corresponding to each transport layer as the candidate PMI corresponding to the transport layer.

[0012] In one embodiment, determining the target rank identifier based on each first MI includes: using the rank identifier corresponding to the largest first MI among the first MIs as the target rank identifier.

[0013] In one embodiment, determining the corresponding first mutual information (MI) based on each preprocessed signal-to-noise ratio (SNR) data includes: for each preprocessed SNR data, calculating the ratio between the preprocessed SNR data and the number of transport layers to obtain a first ratio; multiplying the first ratio, the conjugate transpose of the effective channel data of the antenna, and the effective channel data of the antenna to obtain a first product; adding the first product to a preset identity matrix to obtain a first sum; and calculating the logarithm of the first sum to obtain the first MI corresponding to the preprocessed SNR data.

[0014] In one embodiment, determining the second MI of the transmission layer under different amplitude modulation schemes based on the post-processed signal-to-noise ratio data corresponding to each transmission layer includes: searching for the corresponding second MI in the mapping table corresponding to each amplitude modulation scheme based on the post-processed signal-to-noise ratio data corresponding to each transmission layer; wherein, the mapping table corresponding to each amplitude modulation scheme records the mapping relationship between the post-processed signal-to-noise ratio data and the second MI.

[0015] Secondly, this application provides an rank information determination device, comprising:

[0016] The data acquisition module is used to acquire the preprocessed signal-to-noise ratio data of the receiving antenna under different channel spatiotemporal parameters, as well as the postprocessed signal-to-noise ratio data of the receiving antenna at different transmission layers.

[0017] The first determining module is used to determine the corresponding first mutual information (MI) based on each preprocessed signal-to-noise ratio (SNR) data.

[0018] The second determining module is used to determine the second MI of the transmission layer under different amplitude modulation methods based on the post-processed signal-to-noise ratio data corresponding to each transmission layer;

[0019] The third determining module is used to determine the target rank information of the receiving antenna based on each first MI and each second MI.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method provided in the first aspect.

[0021] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect.

[0022] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in the first aspect.

[0023] The aforementioned method, apparatus, device, medium, and product for determining rank information determine the first MI corresponding to each preprocessed SNR data based on the preprocessed SNR data of the receiving antenna under different channel spatiotemporal parameters. It is evident that the first MI reflects the corresponding channel spatiotemporal parameters. Based on the post-processed SNR data of the receiving antenna at each transmission layer, the second MI is determined for that transmission layer under different amplitude modulation schemes. It is evident that the second MI reflects the amplitude modulation scheme used. Since the first MI reflects the channel spatiotemporal parameters and the second MI reflects the amplitude modulation scheme used, using the first and second MIs as two measures considers multiple factors. Therefore, appropriate target rank information can be determined based on each first MI and each second MI, improving the reliability of the target rank information and enhancing the transmission performance of the multi-input multi-output antenna system. Furthermore, this embodiment does not require a deep learning model, thus avoiding problems such as high sample acquisition costs and insufficient generalization ability. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1A This is a flowchart illustrating a method for determining rank information in one embodiment;

[0026] Figure 1B This is a schematic diagram illustrating how the first MI changes with the preprocessed signal-to-noise ratio data in one embodiment;

[0027] Figure 1C This is a schematic diagram illustrating the subsequent processing of signal-to-noise ratio data changes by the second MI in one embodiment;

[0028] Figure 2 This is a flowchart illustrating the target rank information determination steps in one embodiment;

[0029] Figure 3 This is a flowchart illustrating the candidate PMI determination steps in one embodiment;

[0030] Figure 4 This is a flowchart illustrating the target rank identifier determination step in one embodiment;

[0031] Figure 5This is a flowchart illustrating the first MI determination step in one embodiment;

[0032] Figure 6 This is a flowchart illustrating the second MI determination step in one embodiment;

[0033] Figure 7 This is a flowchart illustrating the signal-to-noise ratio data acquisition steps in one embodiment;

[0034] Figure 8 This is a flowchart illustrating a method for determining rank information in one embodiment;

[0035] Figure 9 This is a structural block diagram of a rank information determination device in one embodiment;

[0036] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] In one exemplary embodiment, a method for determining rank information is provided, see [link to relevant documentation]. Figure 1A The method includes the following steps:

[0039] S110: Obtain the preprocessed signal-to-noise ratio (SNR) data of the receiving antenna under different channel spatiotemporal parameters, and the postprocessed SNR data of the receiving antenna at different transmission layers.

[0040] Among them, the channel spatiotemporal parameters may include channel type parameters and / or channel spatial correlation parameters, and of course may include other parameters, which are not limited here.

[0041] For example, for a multiple-input multiple-output (MIMO) antenna system with 4 transmission layers for the receiving antenna and 32 transmission layers for the transmitting antenna, and a propagation channel model of TDL (Tapped Delay Line Model), the channel type parameters can include five types: TDL-A (Tapped Delay Line Model-A), TDL-B (Tapped Delay Line Model-B), TDL-C (Tapped Delay Line Model-C), TDL-D (Tapped Delay Line Model-D), and TDL-E (Tapped Delay Line Model-E). The channel spatial correlation parameters can include ULA_Low (Uniform Linear Array - Low Spatial Correlation) and ULA_Medium (Uniform Linear Array - Medium). There are eight types: SpatialCorrelation (Uniform Linear Array - Medium Spatial Correlation), ULA_MediumA (Uniform Linear Array - Medium Variant A Spatial Correlation), ULA_High (Uniform Linear Array - High Spatial Correlation), XP1D_Medium (Cross-Polarized 1D Array - Medium Spatial Correlation), XP1D_High (Cross-Polarized 1D Array - High Spatial Correlation), XP2D_Medium (Cross-Polarized 2D Array - Medium Spatial Correlation), and XP2D_High (Cross-Polarized 2D Array - High Spatial Correlation).

[0042] Among them, the preprocessing signal-to-noise ratio data can be understood as the raw signal-to-noise ratio directly observed by the receiving antenna before precoding. It reflects the fading characteristics of the channel itself and the initial impact of noise on the signal, and is the basic reference data for precoding.

[0043] The post-processed signal-to-noise ratio (SNR) data can be understood as the SNR recovered by the receiving antenna after precoding and detection of the received signal. It reflects the compensation effect of the precoding and detection algorithms on channel impairment.

[0044] Understandably, preprocessed signal-to-noise ratio (SNR) data is a "snapshot" of the initial channel quality, used to guide precoding; postprocessed SNR data is the actual performance after optimization of precoding and detection algorithms, reflecting the capacity and reliability of the multiple-input multiple-output (MIMO) antenna system.

[0045] Understandably, different channel spatiotemporal parameters correspond to different preprocessing signal-to-noise ratio (SNR) data. The receiving antenna has multiple transmission layers, and the corresponding post-processing SNR data can be determined for each transmission layer. Therefore, through S110, we can obtain the preprocessing SNR data corresponding to multiple channel spatiotemporal parameters, as well as the post-processing SNR data corresponding to multiple transmission layers.

[0046] S120, determine the corresponding first mutual information MI based on each preprocessed signal-to-noise ratio data.

[0047] Mutual information (MI) is used to quantify the statistical dependence between two random variables, that is, the amount of information about one variable contained in another.

[0048] The first mutual information can be symbol mutual information. Symbol mutual information reflects the degree of correlation between two symbols. That is, it reveals the strength of the correlation between the two by quantifying the amount of information contained in one symbol about another, and measures the amount of information shared between them. In other words, symbol mutual information measures the mutual information between the transmitted and received symbols, directly reflecting the channel's ability to transmit symbols. It is suitable for high-order modulation scenarios and is used to evaluate channel capacity and other performance characteristics.

[0049] Understandably, since the first MI corresponding to each preprocessed signal-to-noise ratio data can be obtained in S120, and different preprocessed signal-to-noise ratio data correspond to different channel spatiotemporal parameters, the first MI can reflect the corresponding channel spatiotemporal parameters.

[0050] Different first MIs can effectively characterize spatial correlation; for example, see [reference needed]. Figure 1B When the channel type parameter is TDL-A, the relationship between the magnitudes of the first MI under different channel spatial correlation parameters is as follows:

[0051] ULA_Low>XP2D_Medium>XP1D_Medium>XP2D_High>XP1D_High>ULA_MediumA>ULA_Medium>ULA_High.

[0052] As can be seen, the magnitude relationship of the first MI can well reflect the spatial correlation strength of the multiple input multiple output antenna system.

[0053] Among them, TDL-A, TDL-B, and TDL-C are all NLOS (Non-Line-of-Sight) channels, with little distinction in the first MI (First Interference Scale). TDL-D and TDL-E are LOS (Line-of-Sight) channels, with some distinction in the first MI. Overall, the first MI of LOS channels is smaller than that of NLOS channels because LOS channels have a direct path and stronger correlation. Therefore, using the first MI as a metric allows for a quantitative comparison of the spatial correlation of the effective channels after precoding.

[0054] S130, based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, determine the second MI of the transmission layer under different amplitude modulation methods.

[0055] Among them, the amplitude modulation method can be Quadrature Amplitude Modulation (QAM). QAM is an efficient digital modulation technique that combines amplitude modulation and phase modulation, transmitting multiple bits of information by simultaneously changing the amplitude and phase of the carrier.

[0056] The types of QAM modes can include five types: 4QAM (4-Quadrature Amplitude Modulation), 16QAM (16-Quadrature Amplitude Modulation), 64QAM (64-Quadrature Amplitude Modulation), 256QAM (256-Quadrature Amplitude Modulation), and 1024QAM (1024-Quadrature Amplitude Modulation). Of course, other types can also be included, but this is not limited here.

[0057] The second MI can be the bit MI, which measures the mutual information between the log-likelihood ratio of the transmitted bits and the received symbols. It is suitable for soft decoding scenarios and reflects the amount of effective information provided by the channel to the decoder.

[0058] As can be seen, for each transmission layer, multiple amplitude modulation methods can be identified as corresponding to the second MI, and the amplitude modulation method used can be reflected through the second MI.

[0059] For example, see Figure 1C The graph shows the changes in the signal-to-noise ratio (SNR) data after the second MI (second MI) processing under five tuning modes: 4QAM, 16QAM, 64QAM, 256QAM, and 1024QAM. Under the same SNR conditions, the higher the QAM order, the smaller the second MI, and the lower the reliability, which is consistent with theory. Therefore, using the second MI as a reliable metric allows for a quantitative comparison of the achievable rate of the effective channel after precoding.

[0060] S140, determine the target rank information of the receiving antenna based on each first MI and each second MI.

[0061] The target rank information includes the target rank identifier and the candidate precoding matrix identifier.

[0062] Among them, the Rank Indication (RI) represents the rank of the spatial channel, that is, the number of the largest indistinguishable uncorrelated data transmission channels between the transmitting antenna and the receiving antenna.

[0063] Among them, the candidate precoding matrix indicator (PMI) is a feedback from the user terminal to the downlink channel state, indicating to the base station which precoding matrix should be selected for signal transmission.

[0064] In this embodiment, based on the preprocessed signal-to-noise ratio (SNR) data of the receiving antenna under different channel spatiotemporal parameters, a first MI corresponding to each preprocessed SNR data is determined. It is evident that the first MI reflects the corresponding channel spatiotemporal parameters. Based on the post-processed SNR data of the receiving antenna at each transmission layer, a second MI for that transmission layer under different amplitude modulation schemes is determined. It is evident that the second MI reflects the amplitude modulation scheme used. Since the first MI reflects the channel spatiotemporal parameters and the second MI reflects the amplitude modulation scheme used, using the first and second MIs as two measures considers multiple factors. Therefore, appropriate target rank information can be determined based on each first MI and each second MI, improving the reliability of the target rank information and enhancing the transmission performance of the multiple-input multiple-output antenna system. Furthermore, this embodiment does not require a deep learning model, thus avoiding problems such as high sample acquisition costs and insufficient generalization ability.

[0065] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the target rank information determination step in S140 is refined.

[0066] See Figure 2 The detailed steps for determining the target rank information include:

[0067] S210, determine the candidate precoding matrix identifier PMI corresponding to each transport layer based on each second MI corresponding to each transport layer.

[0068] In this context, the candidate PMI corresponding to each transport layer can be the optimal PMI corresponding to that transport layer, thereby improving the reliability of the target PMI.

[0069] Understandably, multiple candidate PMIs can be obtained for multiple transport layers, thus forming a candidate PMI set.

[0070] In one alternative implementation, see Figure 3 The candidate PMI determination step in S210 may include:

[0071] S310, take the PMI corresponding to the largest second MI among all the second MIs corresponding to each transport layer as the candidate PMI corresponding to the transport layer.

[0072] As can be seen, for each transmission layer, each second MI in the transmission layer embodies a different amplitude modulation method. The largest second MI is selected from the second MIs corresponding to the transmission layer, and then the PMI corresponding to the largest second MI is used as the candidate PMI for the transmission layer. The candidate PMI embodies the most suitable amplitude modulation method for the transmission layer.

[0073] For example, the candidate PMI corresponding to the i-th transport layer can be calculated according to the following formula:

[0074]

[0075] Among them, PMI i Opt N is the candidate PMI corresponding to the i-th transport layer; BitMI is the second MI; RX The number of transmission layers, i.e., the number of receiving antennas, where i is greater than or equal to 1 and less than or equal to N. RX A positive integer; CodebookSet is the identifier of each precoding matrix in the codebook set specified by the protocol; arg max() represents the argument that makes the function reach its maximum value.

[0076] The above implementation method, based on the maximum second MI corresponding to each transport layer, can accurately select the optimal PMI corresponding to that transport layer as a candidate PMI. The candidate PMI set formed by all candidate PMIs provides a smaller search range for subsequent selection of the target PMI.

[0077] S220, determine the target rank identifier based on each first MI, and take the candidate PMI corresponding to the transport layer where the target rank identifier is located as the target PMI.

[0078] Understandably, each first MI can reflect different channel spatiotemporal parameters, so the target rank identifier corresponding to the most suitable channel spatiotemporal parameters can be determined based on each first MI.

[0079] In one alternative implementation, see Figure 4 The target rank identifier determination step in S220 may include:

[0080] S410, take the rank identifier corresponding to the largest first MI among all first MIs as the target rank identifier.

[0081] It is evident that different first MIs can reflect different channel spatiotemporal parameters. The largest MI is selected from all the first MIs, and then the rank identifier corresponding to the largest MI is used as the target rank identifier. This target rank identifier can reflect the most suitable channel spatiotemporal parameters.

[0082] For example, the target rank identifier and target PMI can be calculated according to the following formula:

[0083]

[0084] in, For the target PMI, For the target rank identifier, For the first MI, This represents the set of candidate PMIs mentioned above.

[0085] It can be seen that the rank identifier corresponding to the largest first MI is taken as the target rank identifier, and then the candidate PMI corresponding to the transport layer i where the target rank identifier is located is taken as the target PMI.

[0086] In the above implementation, the optimal rank identifier can be accurately selected as the target rank identifier based on the largest first MI among all first MIs.

[0087] In this embodiment, based on each second MI corresponding to each transport layer, a candidate precoding matrix identifier (PMI) corresponding to the transport layer is determined, thus forming a candidate PMI set. This provides a smaller search range for the subsequent determination of the target PMI, improving the efficiency of target PMI determination. Then, based on each first MI, the target rank identifier is determined. It is only necessary to use the candidate PMI corresponding to the transport layer where the target rank identifier is located as the target PMI. Therefore, this embodiment effectively decouples the determination process of the target PMI and the target rank identifier. Compared to the joint determination method of the target PMI and the target rank identifier, this embodiment reduces the complexity of rank information determination.

[0088] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the first MI determination step in S120 is refined.

[0089] See Figure 5 The first MI determination step includes:

[0090] S510: For each preprocessed signal-to-noise ratio data, calculate the ratio between the preprocessed signal-to-noise ratio data and the number of transmission layers to obtain the first ratio.

[0091] The first ratio can be expressed as:

[0092]

[0093] In the formula, PreSNR represents the preprocessed signal-to-noise ratio data. This represents the number of transmission layers, i.e., the number of receiving antennas.

[0094] S520, multiply the first ratio, the conjugate transpose of the effective channel data of the antenna, and the effective channel data of the antenna to obtain the first product.

[0095] The first product can be expressed as:

[0096]

[0097] In the formula, For effective channel data of the antenna, This is the conjugate transpose of the effective channel data of the antenna, with H in the upper right corner representing the conjugate transpose symbol.

[0098] S530, add the first product to the preset identity matrix to obtain the first sum.

[0099] The size of the preset identity matrix can be .

[0100] The first sum can be expressed as:

[0101]

[0102] In the formula, For size The identity matrix.

[0103] S540, calculate the logarithm of the first sum to obtain the first MI corresponding to the preprocessed signal-to-noise ratio data.

[0104] The logarithm of the first sum can be calculated using a base-2 logarithmic function.

[0105] Therefore, the calculation formula for the first MI in this embodiment can be expressed as:

[0106]

[0107] in, For the first MI.

[0108] In this embodiment, in determining the first MI, in addition to considering the preprocessed signal-to-noise ratio data, the effective channel data of the antenna is also considered. Therefore, the first MI that can reflect the spatial correlation characteristics after precoding can be determined.

[0109] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which the second MI determination step in S130 is refined.

[0110] See Figure 6 The second MI determination step includes:

[0111] S610: Based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, look up the corresponding second MI in the mapping table corresponding to different amplitude modulation methods.

[0112] The mapping table for each amplitude modulation method records the mapping relationship between the post-processed signal-to-noise ratio data and the second MI.

[0113] Understandably, the formula for calculating the second MI can be expressed as:

[0114]

[0115] In the formula, For the second MI, This is the post-processed signal-to-noise ratio data corresponding to the i-th transport layer. This represents the mapping table corresponding to the Mth type of amplitude modulation method.

[0116] In this embodiment, the detection method of the multi-input multi-output antenna system is the linear minimum mean square error (MMSE) detection method, and it does not consider inter-flow interference caused by spatial correlation.

[0117] Therefore, the second MI can be understood as the achievable rate of the effective channel after considering the detection method and amplitude modulation method of the multiple input multiple output antenna system.

[0118] In this embodiment, the corresponding second MI can be accurately and quickly determined by searching in the mapping tables corresponding to different amplitude modulation methods.

[0119] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided. In this optional embodiment, the signal-to-noise ratio data acquisition step in S110 may include:

[0120] See Figure 7 The signal-to-noise ratio data acquisition steps may include:

[0121] S710 performs channel estimation under different channel spatiotemporal parameters based on the received signal and the reference signal, and obtains the corresponding channel estimation data.

[0122] The received signal is an electromagnetic wave signal captured from space. The received signal may contain user data, noise, interference, and distortion caused by the channel.

[0123] The reference signal is a known signal pre-agreed upon by the receiving and transmitting antennas, used to assist in operations such as channel estimation, synchronization, and phase correction.

[0124] In one alternative implementation, the channel parameter estimation process may include coarse channel estimation, channel parameter estimation, and fine channel estimation, and may also include other estimation processes, which are not limited here.

[0125] Understandably, coarse channel estimation is the initial stage of obtaining channel state information in a wireless communication system. Its core objective is to preliminarily estimate the channel response through a fast and low-complexity algorithm, providing an initial reference for subsequent processing steps.

[0126] The coarse channel estimation process takes the received signal and the reference signal as inputs. Under different channel spatiotemporal parameters, the output is the channel frequency response data corresponding to each parameter, which serves as the coarse channel estimation data. For example, the least squares method can be used to implement the coarse channel estimation process.

[0127]

[0128] In the formula, y is the input signal and s is the reference signal. This is channel frequency response data.

[0129] The purpose of channel parameter estimation is to obtain the parameters needed to construct the filter, i.e., the filter parameters, based on the coarse channel estimation data output by the coarse channel estimation process, and then construct the filter according to the filter parameters. The filter, i.e., the channel filtering method, can be DFT (Discrete Fourier Transform), FD-MMSE (Frequency-Domain Minimum Mean Square Error), or TD-MMSE (Time-Domain Minimum Mean Square Error). The filter parameters can include at least one of noise variance information, time delay offset information, time delay spread information, Doppler offset information, and Doppler spread information.

[0130] The fine channel estimation process is implemented based on the filter constructed from the parameter information output by the channel parameter estimation process and the coarse channel estimation data output by the coarse channel estimation process. It can be expressed by the following calculation formula:

[0131]

[0132] In the formula, The fine channel estimation data output from the fine channel estimation process. For filters, This represents the Hadamard product operation. This is the coarse channel estimation data output from the coarse channel estimation process.

[0133] Among them, coarse channel estimation data and fine channel estimation data All of these are channel estimation data.

[0134] S720 performs noise estimation based on channel estimation data to obtain corresponding noise estimation data.

[0135] The noise estimation data can be the noise covariance matrix, which can be calculated using the following formula:

[0136]

[0137] In the formula, Let H be the noise covariance matrix, with H in the upper right corner indicating the sign of the conjugate transpose.

[0138] S730 performs effective channel processing based on channel estimation data and noise estimation data under the same channel spatiotemporal parameters to obtain the corresponding antenna effective channel data.

[0139] Effective channel processing may include noise whitening and effective channel matrix calculation. Other processing steps may also be included, but are not limited here.

[0140] The purpose of noise whitening is to reduce colored noise between antennas. Specifically, it can be achieved by spatial nulling based on the noise covariance matrix, using the following calculation formula:

[0141]

[0142] In the formula, The channel estimation matrix is ​​obtained after noise whitening.

[0143] The effective channel matrix, calculated from the effective channel matrix, represents the equivalent channel estimation matrix after precoding and can be implemented using the following formula:

[0144]

[0145] In the formula, is the effective channel matrix, i.e., the effective channel data of the antenna; P is the precoding matrix, which comes from the codebook set specified by the protocol. Therefore, different effective channel matrices can be obtained based on different precoding matrices in the codebook set specified by the protocol.

[0146] S740 calculates the signal-to-noise ratio (SNR) based on channel estimation data and noise estimation data under the same channel spatiotemporal parameters, and obtains the corresponding preprocessed SNR data.

[0147] The signal-to-noise ratio data can be calculated using the following formula:

[0148]

[0149]

[0150] In the formula, For preprocessing signal-to-noise ratio data, R HH The channel covariance matrix, Represents the channel covariance matrix R HH The elements on the diagonal grid, (R) nn ) ii Represents the noise covariance matrix R nn The elements on the diagonal.

[0151] S750 calculates the signal-to-noise ratio (SNR) based on the preprocessed SNR data and the effective channel data of the antenna under any spatiotemporal parameters, and obtains the post-processed SNR data corresponding to each transmission layer.

[0152] The post-processed signal-to-noise ratio data corresponding to the i-th transport layer can be calculated using the following formula. :

[0153]

[0154] In the formula, X= (X) ii This represents the elements on the diagonal from row 1 to column 1 to column 1 in matrix X.

[0155] In this embodiment, by performing channel estimation, noise estimation, effective channel processing, and signal-to-noise ratio (SNR) calculation, the preprocessed SNR data and postprocessed SNR data can be accurately calculated, which facilitates the accurate calculation of the first MI and the second MI in the subsequent process.

[0156] Based on the technical solutions provided in the above embodiments, an optional embodiment is provided, in which see... Figure 8 Methods for determining rank information include:

[0157] S801 performs coarse channel estimation processing based on the received signal, reference signal, and different channel spatiotemporal parameters to obtain coarse channel estimation data.

[0158] S802 performs channel parameter estimation processing based on coarse channel estimation data to obtain filter parameters.

[0159] S803 performs fine channel estimation processing based on the filter constructed based on the filter parameters and the coarse channel estimation data to obtain fine channel estimation data.

[0160] S804 performs noise estimation based on the channel estimation data to obtain the corresponding noise estimation data.

[0161] S805 performs effective channel processing based on channel estimation data and noise estimation data under the same channel spatiotemporal parameters to obtain the corresponding antenna effective channel data.

[0162] S806 calculates the signal-to-noise ratio (SNR) based on channel estimation data and noise estimation data under the same channel spatiotemporal parameters, and obtains the corresponding preprocessed SNR data.

[0163] S807 calculates the signal-to-noise ratio (SNR) based on the preprocessed SNR data and the effective channel data of the antenna under any spatiotemporal parameters, and obtains the post-processed SNR data corresponding to each transmission layer.

[0164] S808: For each preprocessed signal-to-noise ratio (SNR) data, calculate the ratio between the preprocessed SNR data and the number of transmission layers to obtain a first ratio; multiply the first ratio, the conjugate transpose of the effective channel data of the antenna, and the effective channel data of the antenna to obtain a first product; add the first product to a preset identity matrix to obtain a first sum; calculate the logarithm of the first sum to obtain the first MI corresponding to the preprocessed SNR data.

[0165] S809: Based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, look up the corresponding second MI in the mapping table corresponding to different amplitude modulation methods; wherein, the mapping table corresponding to each amplitude modulation method records the mapping relationship between the post-processed signal-to-noise ratio data and the second MI.

[0166] S810, the PMI corresponding to the largest second MI among all the second MIs corresponding to each transport layer is taken as the candidate PMI corresponding to the transport layer.

[0167] S811, take the rank identifier corresponding to the largest first MI among all first MIs as the target rank identifier, and take the candidate PMI corresponding to the transport layer where the target rank identifier is located as the target PMI.

[0168] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0169] Based on the same inventive concept, this application also provides a rank information determining apparatus for implementing the rank information determining method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more rank information determining apparatus embodiments provided below can be found in the limitations of the rank information determining method described above, and will not be repeated here.

[0170] In one exemplary embodiment, a rank information determination device is provided, see [link to relevant documentation]. Figure 9 The device includes: a data acquisition module 910, a first determination module 920, a second determination module 930, and a third determination module 940, wherein:

[0171] The data acquisition module 910 is used to acquire the preprocessed signal-to-noise ratio data of the receiving antenna under different channel spatiotemporal parameters, as well as the postprocessed signal-to-noise ratio data of the receiving antenna at different transmission layers.

[0172] The first determining module 920 is used to determine the corresponding first mutual information MI based on each preprocessed signal-to-noise ratio data.

[0173] The second determining module 930 is used to determine the second MI of the transmission layer under different amplitude modulation methods based on the post-processed signal-to-noise ratio data corresponding to each transmission layer;

[0174] The third determining module 940 is used to determine the target rank information of the receiving antenna based on each first MI and each second MI.

[0175] In one embodiment, the third determining module includes:

[0176] The first determining unit is used to determine the candidate precoding matrix identifier (PMI) corresponding to each transport layer based on each second MI corresponding to each transport layer.

[0177] The second determining unit is used to determine the target rank identifier based on each first MI, and to take the candidate PMI corresponding to the transport layer where the target rank identifier is located as the target PMI.

[0178] In one embodiment, the first determining unit is specifically used to: take the PMI corresponding to the largest second MI among all second MIs corresponding to each transport layer as the candidate PMI corresponding to the transport layer.

[0179] In one embodiment, the second determining unit is specifically used to: take the rank identifier corresponding to the largest first MI among the first MIs as the target rank identifier.

[0180] In one embodiment, the first determining module is specifically configured to: for each preprocessed signal-to-noise ratio (SNR) data, calculate the ratio between the preprocessed SNR data and the number of transmission layers to obtain a first ratio; multiply the first ratio, the conjugate transpose of the effective channel data of the antenna, and the effective channel data of the antenna to obtain a first product; add the first product to a preset identity matrix to obtain a first sum; and calculate the logarithm of the first sum to obtain the first MI corresponding to the preprocessed SNR data.

[0181] In one embodiment, the second determining module is specifically used to: search for the corresponding second MI in the mapping table corresponding to different amplitude modulation methods according to the post-processed signal-to-noise ratio data corresponding to each transmission layer; wherein, the mapping table corresponding to each amplitude modulation method records the mapping relationship between the post-processed signal-to-noise ratio data and the second MI.

[0182] Each module in the aforementioned rank information determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0183] In one exemplary embodiment, a computer device is provided, the internal structure of which can be as shown in the figure. Figure 10 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for determining rank information.

[0184] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0185] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the rank information determination method provided in the above embodiments.

[0186] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the rank information determination method provided in the above embodiments.

[0187] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the rank information determination method provided in the above embodiments.

[0188] The rank information determination methods provided in the above embodiments can be executed by a chip or a chip module; the rank information determination devices provided in the above embodiments can be chips or chip modules. Regarding the modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for various devices and products applied to or integrated into a chip, all included modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs running on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits; for various devices and products applied to or integrated into a chip module, all included modules / units can be implemented using hardware methods such as circuits, and different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The components can be implemented using software programs that run on the processor integrated within the chip module. The remaining (if any) modules / units can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into the terminal, each of its components / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on the processor integrated within the terminal, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0191] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0192] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining rank information, characterized in that, include: The method acquires preprocessed signal-to-noise ratio (SNR) data of the receiving antenna under different channel spatiotemporal parameters, and postprocessed SNR data of the receiving antenna at different transmission layers; wherein, the preprocessed SNR data is the original SNR observed by the receiving antenna before precoding the received signal; and the postprocessed SNR data is the signal SNR recovered by the receiving antenna after precoding and detection of the received signal. Based on each preprocessed signal-to-noise ratio data, determine the corresponding first mutual information (MI); Based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, the second MI of the transmission layer under different amplitude modulation methods is determined; Based on each of the second MIs corresponding to each transport layer, the candidate precoding matrix identifier (PMI) corresponding to the transport layer is determined; Based on each of the first MIs, a target rank identifier is determined, and the candidate PMI corresponding to the transport layer where the target rank identifier is located is taken as the target PMI.

2. The method according to claim 1, characterized in that, The step of determining the candidate precoding matrix identifier (PMI) corresponding to each transport layer based on each of the second MIs corresponding to each transport layer includes: The PMI corresponding to the largest second MI among all the second MIs corresponding to each transport layer is taken as the candidate PMI corresponding to the transport layer.

3. The method according to claim 1, characterized in that, The step of determining the target rank identifier based on each of the first MIs includes: The rank identifier corresponding to the largest first MI among all the first MIs is used as the target rank identifier.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the corresponding first mutual information (MI) based on each preprocessed signal-to-noise ratio (SNR) data includes: For each preprocessed signal-to-noise ratio data, the ratio between the preprocessed signal-to-noise ratio data and the number of transport layers is calculated to obtain a first ratio. Multiply the first ratio, the conjugate transpose of the effective channel data of the antenna, and the effective channel data of the antenna to obtain the first product; Add the first product to the preset identity matrix to obtain the first sum; Calculate the logarithm of the first sum to obtain the first MI corresponding to the preprocessed signal-to-noise ratio data.

5. The method according to any one of claims 1-3, characterized in that, The step of determining the second MI of the transmission layer under different amplitude modulation schemes based on the post-processed signal-to-noise ratio data corresponding to each transmission layer includes: Based on the post-processed signal-to-noise ratio data corresponding to each transmission layer, the corresponding second MI is found in the mapping table corresponding to different amplitude modulation methods. The mapping table for each amplitude modulation method records the mapping relationship between the post-processed signal-to-noise ratio data and the second MI.

6. A rank information determination device, characterized in that, include: The data acquisition module is used to acquire preprocessed signal-to-noise ratio (SNR) data of the receiving antenna under different channel spatiotemporal parameters, and postprocessed SNR data of the receiving antenna at different transmission layers; wherein, the preprocessed SNR data is the original SNR observed by the receiving antenna before precoding the received signal; and the postprocessed SNR data is the signal SNR recovered by the receiving antenna after precoding and detection of the received signal. The first determining module is used to determine the corresponding first mutual information (MI) based on each preprocessed signal-to-noise ratio (SNR) data. The second determining module is used to determine the second MI of the transmission layer under different amplitude modulation methods based on the post-processed signal-to-noise ratio data corresponding to each transmission layer; The third determining module includes a first determining unit, used to determine the candidate precoding matrix identifier (PMI) corresponding to each transport layer based on each second MI corresponding to each transport layer; and a second determining unit, used to determine the target rank identifier based on each first MI, and to take the candidate PMI corresponding to the transport layer where the target rank identifier is located as the target PMI.

7. The apparatus according to claim 6, characterized in that, The first determining unit is specifically used to: take the PMI corresponding to the largest second MI among all the second MIs corresponding to each transport layer as the candidate PMI corresponding to the transport layer.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-5.

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