A channel assessment method and related device

Abstract

The present application discloses a channel assessment method and related devices, relating to the field of communication technology, the method comprising: initializing auxiliary manifold natural parameters and statistical information, and setting a damping value; executing the following loop steps until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold: determining the m-projection of this round based on the statistical information and the auxiliary manifold natural parameters of the previous round; determining the auxiliary manifold natural parameters of this round based on the m-projection of this round, the auxiliary manifold natural parameters of the previous round, and the damping value; determining the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round; determining the statistical information of this round based on the statistical information of the previous round and the target manifold natural parameters of this round; finally, determining the beam domain channel assessment result based on the target manifold natural parameters of the last round and the statistical information of the last round, the channel assessment result having higher accuracy.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a channel assessment method and related devices. Background Art

[0002] Massive Multiple-Input Multiple-Output (MIMO) technology is considered one of the core technologies of fifth-generation cellular communication systems. In a massive MIMO system, base stations deploy a large number of antennas, enabling them to simultaneously serve a large number of users and provide high-speed data transmission services using the same time and frequency resources. This design not only greatly increases system capacity but also significantly improves energy efficiency. On the other hand, Orthogonal Frequency Division Multiplexing (OFDM), a multi-carrier modulation scheme, effectively mitigates the effects of frequency-selective fading in broadband wireless communications. Currently, the combination of massive MIMO and OFDM, namely massive MIMO-OFDM technology, has already played a vital role in 5G communication systems and is attracting significant attention in the development of future sixth-generation (6G) systems.

[0003] In massive MIMO-OFDM systems, the accuracy of channel estimation is crucial to system performance. To achieve efficient channel estimation, a pilot-based method is usually adopted, that is, the transmitter periodically sends a pilot signal, and the receiver estimates the channel state information (CSI) based on the received pilot signal, that is, obtains the posterior information of the channel. When the prior distribution of the channel parameters follows a Gaussian distribution, its posterior distribution will also be Gaussian. In this case, the posterior information of the channel parameters can be characterized by the mean and covariance matrix. However, in massive MIMO-OFDM systems, due to the extremely high channel dimensionality, calculating these posterior statistics becomes extremely challenging. Traditional estimation methods, such as the minimum mean square error (MMSE) estimator, often have unacceptable computational complexity due to the need to process large-scale matrix inversion operations.

[0004] The information geometry (IG) channel estimation algorithm has the advantage of low complexity. Based on the statistical channel model of space-frequency (SF) beams, the IG algorithm transforms the channel estimation process into an iterative projection process. It has been proven that the IG algorithm can accurately estimate the posterior mean at its stable point. To further reduce computational complexity, a simplified information geometry (SIG) algorithm was proposed based on the IG algorithm. The SIG algorithm exploits the constant amplitude characteristics of the measurement matrix elements and derives several key properties of the natural parameters. These properties simplify the iterative process of the IG algorithm, thus forming the SIG algorithm. Notably, the posterior mean estimated by the SIG algorithm is asymptotically optimal.

[0005] However, both the IG and SIG algorithms require accurate SF beam-domain statistical CSI before instantaneous channel estimation, which is often difficult to achieve in practice. Traditional statistical CSI acquisition methods use the estimated instantaneous channel to calculate statistical CSI. Therefore, it is impossible to obtain relatively accurate statistical CSI before channel estimation, which limits the practicality of the SIG algorithm and reduces channel estimation performance. Summary of the Invention

[0006] In view of this, the main purpose of this application is to provide a channel assessment method, apparatus, device, program product and readable storage medium to improve channel assessment performance.

[0007] A first aspect of the present application provides a channel assessment method, the method comprising:

[0008] Initialize the auxiliary manifold natural parameters and statistical information, and set the damping value;

[0009] The following steps are executed in a loop until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold: determining the m-projection of this round based on the statistical information of the previous round and the auxiliary manifold natural parameters of the previous round; determining the auxiliary manifold natural parameters of this round based on the m-projection of this round, the auxiliary manifold natural parameters of the previous round and the damping value; determining the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round; determining the statistical information of this round based on the statistical information of the previous round and the target manifold natural parameters of this round; determining the target manifold parameter change rate based on the target manifold natural parameters of this round and the target manifold natural parameters of the previous round; determining the statistical information change rate based on the statistical information of this round and the statistical information of the previous round;

[0010] In response to the target manifold parameter change rate or the statistical information change rate being less than a preset threshold, the mean of the target manifold is determined based on the natural parameters of the target manifold of the last round and the statistical information of the last round, and the mean of the target manifold is used as the beam domain channel evaluation result.

[0011] In some implementations of the first aspect of the present application, the method further includes:

[0012] The beam domain channel estimation results are converted into space-frequency domain channel estimation results.

[0013] In some implementations of the first aspect of the present application, the auxiliary manifold natural parameter includes: an auxiliary manifold first-order average natural parameter and an auxiliary manifold second-order average natural parameter;

[0014] Based on the statistical information of the previous round and the natural parameters of the auxiliary manifold of the previous round, the m-projection of this round is determined, including:

[0015] The first m-projection of this round is determined according to the statistical information of the previous round and the first-order average natural parameters of the auxiliary manifold of the previous round; the second m-projection of this round is determined according to the statistical information of the previous round and the second-order average natural parameters of the auxiliary manifold of the previous round.

[0016] In some implementations of the first aspect of the present application, determining the auxiliary manifold natural parameters of the current round according to the m-projection of the current round, the auxiliary manifold natural parameters of the previous round, and the damping value includes:

[0017] The first-order average natural parameters of the auxiliary manifold of this round are determined based on the first m projections of this round, the first-order average natural parameters of the auxiliary manifold of the previous round, and the damping value; the second-order average natural parameters of the auxiliary manifold of this round are determined based on the second m projections of this round, the second-order average natural parameters of the auxiliary manifold of the previous round, and the damping value.

[0018] In some implementations of the first aspect of the present application, the target manifold natural parameters include: first-order natural parameters of the target manifold and second-order natural parameters of the target manifold;

[0019] According to the natural parameters of the auxiliary manifold of this round, the natural parameters of the target manifold of this round are determined, including:

[0020] The first-order natural parameters of the target manifold of this round are determined based on the first-order average natural parameters of the auxiliary manifold of this round; the second-order natural parameters of the target manifold of this round are determined based on the second-order average natural parameters of the auxiliary manifold of this round.

[0021] In some implementations of the first aspect of the present application, determining the statistical information of the current round based on the statistical information of the previous round and the natural parameters of the target manifold of the current round includes:

[0022] The statistical information of this round is determined according to the statistical information of the previous round, the first-order natural parameters of the target manifold of this round, and the second-order natural parameters of the target manifold of this round.

[0023] In some implementations of the first aspect of the present application, determining the mean of the target manifold according to the natural parameters of the target manifold in the last round and the statistical information of the Tth round includes:

[0024] The mean of the target manifold is determined according to the first-order natural parameters of the target manifold in the last round, the second-order natural parameters of the target manifold in the last round, and the statistical information of the Tth round.

[0025] A second aspect of the present application provides a channel assessment device, the device comprising:

[0026] Initialization module, used to initialize the natural parameters and statistical information of the auxiliary manifold and set the damping value;

[0027] a loop execution module, configured to execute the following loop steps until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold: determining the m-projection of this round based on the statistical information of the previous round and the auxiliary manifold natural parameters of the previous round; determining the auxiliary manifold natural parameters of this round based on the m-projection of this round, the auxiliary manifold natural parameters of the previous round, and the damping value; determining the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round; determining the statistical information of this round based on the statistical information of the previous round and the target manifold natural parameters of this round; determining the target manifold parameter change rate based on the target manifold natural parameters of this round and the target manifold natural parameters of the previous round; determining the statistical information change rate based on the statistical information of this round and the statistical information of the previous round;

[0028] The channel evaluation module is used to determine the mean of the target manifold based on the natural parameters of the target manifold and the statistical information of the last round in response to the target manifold parameter change rate or the statistical information change rate being less than a preset threshold, and use the mean of the target manifold as the beam domain channel evaluation result.

[0029] A third aspect of the present application provides a channel assessment device, which includes a memory and a processor, and the processor is used to execute a program stored in the memory and run a channel assessment method as described in any one of the first aspects.

[0030] A fourth aspect of the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the channel assessment methods in the first aspect.

[0031] In a fifth aspect, the present application provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the various steps of any channel assessment method in the first aspect.

[0032] The technical solution provided by this application has the following beneficial effects:

[0033] In the technical solution provided in the present application, first, initialization is performed to provide initial conditions for subsequent iterations; then a loop step is entered to gradually update the auxiliary manifold natural parameters and the target manifold natural parameters until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold.

[0034] Specifically, during the loop, the m-projection for the current round is determined based on the statistical information and the natural parameters of the auxiliary manifold from the previous round. This calculation reduces the complexity of parameter computation. Then, the auxiliary manifold natural parameters for the current round are determined based on the m-projection, the natural parameters of the auxiliary manifold from the previous round, and the damping value. In each iteration, a gradual update controlled by the damping value prevents excessive fluctuations. The post-projection parameter update ensures estimation stability and convergence, helping to improve channel estimation accuracy. Next, the target manifold natural parameters for the current round are determined based on the natural parameters of the auxiliary manifold from the current round. This gradual determination of the target manifold natural parameters during the loop not only simplifies computation but also improves channel estimation accuracy through the correction of the natural parameters during each iteration. Finally, new statistical information is determined based on the statistical information from the previous round and the natural parameters of the target manifold from the current round. By using the updated target manifold natural parameters to update and correct the statistical information, each statistical update further corrects the channel estimate, ensuring that the algorithm converges to the true channel state during the stepwise iterations. At the same time, the updated statistical information continues to be used in the next round of iteration, avoiding the dependence on the previously obtained statistical CSI.

[0035] After the target manifold parameter change rate or the statistical information change rate is less than a preset threshold, the mean of the target manifold is determined as the final channel evaluation result based on the final target manifold natural parameters and statistical information.

[0036] It can be seen that in the above technical solution, the present application does not need to know the beam domain statistics in advance, but updates the statistical information by updating the natural parameters of the target manifold during the cyclic iteration process. Updating the natural parameters of the target manifold only involves multiplication operations of vectors and scalars, so the computational complexity is relatively low. It can be seen that the complexity of the technical solution provided by the present application is not significantly improved compared with the existing technology. It solves the problem that the SIG algorithm needs to know the statistical information in advance without increasing the complexity of the algorithm, and significantly improves the practicality of the SIG algorithm. At the same time, the final channel assessment result has higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of a channel assessment method provided in an embodiment of the present application;

[0038] Figure 2 A flow chart of another channel assessment method provided in an embodiment of the present application;

[0039] Figure 3 A performance comparison chart of the EM-SIG algorithm and the SIG algorithm provided in the embodiments of this application;

[0040] Figure 4This is a statistical information graph estimated by the EM-SIG algorithm provided in an embodiment of the present application;

[0041] Figure 5 A comparison chart of the accuracy of statistical information estimation using the EM-SIG algorithm provided in an embodiment of the present application;

[0042] Figure 6 A schematic structural diagram of a channel assessment device provided in an embodiment of the present application;

[0043] Figure 7 A schematic diagram of the structure of a channel assessment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] Embodiments of the present application provide a channel assessment method, apparatus, device, program product, and readable storage medium.

[0046] See also Figure 1 As shown, an embodiment of the present application provides a channel assessment method, which may specifically include the following steps:

[0047] S101: Initialize the auxiliary manifold natural parameters and statistical information, and set the damping value.

[0048] In the embodiments of this application, an auxiliary manifold is used to approximate some nonlinear terms in the posterior probability density function, characterized by the auxiliary manifold's natural parameters. Statistical information is a priori statistical information that characterizes the channel state during the channel estimation process. The damping value is used to control the update step size of each subsequent iteration to prevent data divergence during the iteration process.

[0049] The embodiment of the present application initializes the natural parameters to provide reasonable initial conditions for subsequent iterations, ensuring that the subsequent cyclic iteration process will not deviate from the target. The setting of the damping value ensures that the subsequent cyclic iteration process can gradually converge to a stable channel estimation result.

[0050] S102: Determine whether the target manifold parameter change rate or the statistical information change rate in the current iteration is less than a preset threshold. If so, execute S108; if not, execute the loop steps of S103-S107.

[0051] In the embodiment of the present application, during each iteration, a determination is made as to whether the target manifold parameter change rate or the statistical information change rate is less than a preset threshold. If the target manifold parameter change rate or the statistical information change rate is less than the preset threshold, the final channel estimation phase is entered, i.e., S108 is executed. Otherwise, S103-S107 are continued for iterative updating. By presetting a reasonable threshold, it is possible to ensure that the iterations terminate properly, avoiding wasting computer resources, while also providing a sufficient number of iterations to converge to the optimal channel estimation result.

[0052] S103: Determine the m-projection of this round based on the statistical information of the previous round and the natural parameters of the auxiliary manifold of the previous round;

[0053] In an embodiment of the present application, m is projected as a distribution on the target manifold, and the distribution satisfies the minimum Kullback-Leibler (KL) divergence to the auxiliary manifold, thereby reducing computational complexity and facilitating improved computational speed in subsequent steps.

[0054] In addition, it can be understood that when this round is the first round, in S103, the statistical information of the previous round is the statistical information initialized in S101, and the auxiliary manifold natural parameters of the previous round are the auxiliary manifold natural parameters initialized in S101.

[0055] The embodiment of the present application calculates the m projections of the current round through the natural parameters and statistical information of the previous round, thereby introducing the projection operation, thereby reducing the complexity of the calculation and avoiding the computational overhead brought by directly processing high-dimensional matrices. At the same time, the average natural parameters of the auxiliary manifold are closer to the natural parameters of the target manifold, so that the estimation of the channel state gradually approaches the actual channel conditions.

[0056] S104: Determine the auxiliary manifold natural parameters of this round according to the m-projection of this round, the auxiliary manifold natural parameters of the previous round, and the damping value.

[0057] In the embodiments of this application, the natural parameters of the auxiliary manifold are updated in the current round based on the m-projection, combined with the natural parameters of the auxiliary manifold from the previous round and a preset damping value. By combining the projection results with the previous round's estimation, the natural parameters of the auxiliary manifold are optimized for the current round, resulting in more accurate channel state estimation. Furthermore, the use of the damping value ensures smoothness and stability in the update process.

[0058] S105: Determine the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round.

[0059] In the embodiments of the present application, the target manifold refers to the space that is ultimately desired to be optimized, and the natural parameters of the target manifold are used to describe the state information of the channel on the target manifold.

[0060] S106: Determine the statistical information of this round based on the statistical information of the previous round and the natural parameters of the target manifold of this round.

[0061] In an embodiment of the present application, the current statistical information is updated using the natural parameters of the target manifold of this round and the statistical information of the previous round. The statistical information can be gradually corrected in each round of iteration, thereby ensuring that the channel information is more accurate and making the final channel estimation closer to the channel characteristics in the real environment.

[0062] S107: Determine the target manifold parameter change rate based on the target manifold natural parameters of this round and the target manifold natural parameters of the previous round; determine the statistical information change rate based on the statistical information of this round and the statistical information of the previous round, and execute S102.

[0063] In an embodiment of the present application, after determining the target manifold natural parameters and statistical information of this round in each iteration process, they will be compared with the target manifold natural parameters and statistical information of the previous round to determine the rate of change of the target manifold natural parameters between the two rounds and the rate of change of the statistical information between the two rounds.

[0064] S108: Determine the mean of the target manifold according to the natural parameters of the target manifold in the last round and the statistical information in the last round, and use the mean of the target manifold as the beam-domain channel evaluation result.

[0065] In an embodiment of the present application, in response to the target manifold parameter change rate or the statistical information change rate being less than a preset threshold, the target manifold natural parameters and statistical information obtained in the last round of iteration are taken, and the mean of the target manifold is calculated as the final beam domain channel evaluation result. That is, the present application estimates the channel state in the beam domain through the mean of the target manifold.

[0066] In some implementations of the embodiments of the present application, S108 further includes the following steps:

[0067] The beam domain channel estimation results are converted into space-frequency domain channel estimation results.

[0068] The beam-domain channel assessment result is the channel state information estimated in the beam domain, which is described in this application as the mean of the target manifold. Compared with the beam domain, the space-frequency domain further incorporates the frequency dimension, so the space-frequency domain channel estimation result refers to the channel state information estimated in the spatial and frequency dimensions. By mapping the channel state in the beam space back to the combined space and frequency space, this application can better adapt to large-scale MIMO-OFDM systems and achieve more accurate channel state feedback.

[0069] exist Figure 1In the process shown, first, initialization is performed to provide initial conditions for subsequent iterations; then a loop step is entered to gradually update the auxiliary manifold natural parameters and the target manifold natural parameters until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold.

[0070] Specifically, during the loop, the m-projection for the current round is determined based on the statistical information and the natural parameters of the auxiliary manifold from the previous round. This calculation reduces the complexity of parameter computation. Then, the auxiliary manifold natural parameters for the current round are determined based on the m-projection, the natural parameters of the auxiliary manifold from the previous round, and the damping value. In each iteration, a gradual update controlled by the damping value prevents excessive fluctuations. The post-projection parameter update ensures estimation stability and convergence, helping to improve channel estimation accuracy. Next, the target manifold natural parameters for the current round are determined based on the natural parameters of the auxiliary manifold from the current round. This gradual determination of the target manifold natural parameters during the loop not only simplifies computation but also improves channel estimation accuracy through the correction of the natural parameters during each iteration. Finally, new statistical information is determined based on the statistical information from the previous round and the natural parameters of the target manifold from the current round. By using the updated target manifold natural parameters to update and correct the statistical information, each statistical update further corrects the channel estimate, ensuring that the algorithm converges to the true channel state during the stepwise iterations. At the same time, the updated statistical information continues to be used in the next round of iteration, avoiding the dependence on the previously obtained statistical CSI.

[0071] After the target manifold parameter change rate or the statistical information change rate is less than a preset threshold, the mean of the target manifold is determined as the final channel evaluation result based on the final target manifold natural parameters and statistical information.

[0072] It can be seen that in the above technical solution, the present application does not need to know the beam domain statistics in advance, but updates the statistical information by updating the natural parameters of the target manifold during the cyclic iteration process. Updating the natural parameters of the target manifold only involves multiplication operations of vectors and scalars, so the computational complexity is relatively low. It can be seen that the complexity of the technical solution provided by the present application is not significantly improved compared with the existing technology. It solves the problem that the SIG algorithm needs to know the statistical information in advance without increasing the complexity of the algorithm, and significantly improves the practicality of the SIG algorithm. At the same time, the final channel assessment result has higher accuracy.

[0073] In terms of system model design, the embodiment of the present application considers a MIMO-OFDM system, where the base station is equipped with N r =N r,v ×N r,h The uniform array of antennas has N antennas in the vertical and horizontal directions. r,v and Nr,h , the number of users is K, and each user is equipped with a single antenna. For OFDM modulation, the number of subcarriers is N c , the system sampling interval is T s , the cyclic prefix length is N g The subcarrier index number used for uplink channel estimation is N p is the number of subcarriers used for channel estimation, N1 and N p are the starting and last subcarrier index numbers respectively, and i is the subcarrier sequence number.

[0074] During uplink transmission, the received signal in the frequency domain is expressed as:

[0075]

[0076] Where Y is the received signal, x k is the pilot signal sent by user k, Z is the noise, is the noise; the noise power is σ.

[0077] Introducing the beam domain channel model:

[0078] G k =VH k F T (2)

[0079] in is the statistical beam domain channel matrix. and is the delay domain sampling matrix, F τ is the delay domain oversampling factor. is the angle domain sampling matrix, where is the angular domain sampling matrix in the vertical direction,

[0080] N v =F v N r,v , F v is the oversampling factor in the vertical direction. is the angle domain sampling matrix in the horizontal direction, N h =F h N r，h , F h is the oversampling factor in the horizontal direction.

[0081] Substituting formula (2) into the received signal model yields:

[0082] Y=VH a M+Z (3)

[0083] The angle delay domain channel Delay Domain Transformation Matrix

[0084] The beam domain energy matrix is ​​defined as:

[0085]

[0086] Due to channel sparsity, many elements in the beam domain energy matrix approach zero. In the algorithm, these low-energy elements can be set to zero.

[0087] Vectorizing formula (3), we get:

[0088] y=Ah+z (5)

[0089] Wherein, the beam domain channel vector h=vec(H a ), sampling matrix Beam domain channel dimension N = N r N p , M=N v N h KN τ . y is obtained by vectorizing Y, and z is obtained by vectorizing Z. Wherein, the prior variance matrix D=Diag(d), d=vec{Ω}.

[0090] The SIG algorithm mentioned in the background art extracts paths with non-zero energy based on statistical information, and the index set of non-zero elements in d is recorded as The extraction matrix is ​​defined as:

[0091]

[0092] Among them, s p is the pth column of the identity matrix, then Simplified sampling matrix in

[0093] This application further introduces the idea of ​​the expectation maximization EM algorithm on the SIG algorithm and proposes a simplified information geometry EM-SIG algorithm based on expectation maximization. The EM-SIG algorithm does not require the calculation of statistical information in advance, so there is no need to extract the path with non-zero energy first. That is, the matrix extracted in formula (6) is E=I M×M .

[0094] In terms of design, the embodiment of the present application introduces the idea of ​​maximizing expectation, treating h as a hidden variable. During the algorithm iteration process, the parameters calculated in the current round of SIG process are combined with the idea of ​​the expectation maximization algorithm to update the statistical information D.

[0095] The statistical information is updated as follows:

[0096]

[0097] The Q function can be written as

[0098]

[0099] According to the Bayesian formula, we can get:

[0100] p(y,h|D)=p(y|h)p(h|D) (9)

[0101] Substituting into formula (8), the Q function can be written as:

[0102]

[0103] In formula (10), the first term is independent of D, and only the second term is related to D, so we can get:

[0104]

[0105] In the SIG algorithm, the elements of h are assumed to be independent complex Gaussian distributions, so lnp(h|D) in Equation (11) is:

[0106]

[0107] Among them, h m =[h] m , d m =[D] m,m , then its partial differential can be calculated as:

[0108]

[0109] In practical applications, the posterior probability is usually not available. However, in the SIG algorithm, the posterior probability can be approximated by the target manifold, so in formula (11), P(h|y,D t ) can be expressed using the target manifold parameters as:

[0110]

[0111] in and It can be obtained in the SIG algorithm, that is

[0112]

[0113]

[0114] Therefore, the partial derivative of the function in formula (11) with respect to D can be calculated as:

[0115]

[0116] The calculation formula (17) can be written as:

[0117]

[0118] in

[0119]

[0120] make We can get:

[0121]

[0122] Rewriting Equation (21) into matrix form, we get:

[0123]

[0124] Where I is the identity matrix, D t is the statistical information of the previous iteration, D t+1 This is the updated statistics for this round. is the second-order natural parameter of the target manifold after this round of iteration, is the first-order natural parameter of the target manifold after this round of iteration.

[0125] In formula (18), when When , we can get the following formula, where is less than real number.

[0126]

[0127] Right now Similarly Sometimes, there are Therefore, formula (22) is the maximum point, which satisfies formula (11).

[0128] See also Figure 2 As shown, an embodiment of the present application provides a channel assessment method. In this embodiment, the auxiliary manifold natural parameters specifically include: the auxiliary manifold first-order average natural parameters and the auxiliary manifold second-order average natural parameters; the m-projection specifically includes: the first m-projection and the second m-projection; the target manifold natural parameters include: the target manifold first-order natural parameters and the target manifold second-order natural parameters; the method specifically may include the following steps:

[0129] S201: Initialize the auxiliary manifold first-order average natural parameters, the auxiliary manifold second-order average natural parameters and statistical information, and set the damping value.

[0130] The first-order average natural parameter is specifically the mean of the channel parameter, and the second-order average natural parameter is the variance of the channel parameter.

[0131] S202: Determine whether the target manifold parameter change rate or the statistical information change rate in the current iteration is less than a preset threshold. If so, execute S208; if not, execute the loop steps of S203-S207.

[0132] Right now:

[0133] Among them, δ1, δ2 and δ3 are preset thresholds.

[0134] S203: Determine the first m projections of this round based on the statistical information of the previous round and the first-order average natural parameters of the auxiliary manifold of the previous round; determine the second m projections of this round based on the statistical information of the previous round and the second-order average natural parameters of the auxiliary manifold of the previous round.

[0135] In some implementations of the embodiments of the present application, the first m projection of this round is calculated by the following first formula (24):

[0136]

[0137] In the first formula (24), I represents the identity matrix, N represents the beam domain channel dimension, A represents the sampling matrix, and D t represents the statistical information of the previous round, σ represents the noise variance, represents the first-order average natural parameter of the auxiliary manifold in the previous round, represents the second-order average natural parameter of the auxiliary manifold in the previous round, tr represents the matrix trace operation, A represents the sampling matrix, y represents the received signal, and A H represents the conjugate transpose of the sampling matrix, represents the first m projection of this round;

[0138] In some implementations of the embodiments of the present application, the second m-projection of this round is calculated by the following second formula (25):

[0139]

[0140] In the second formula (25), N represents the beam domain channel dimension, D t Indicates the statistics of the previous round. represents the second-order average natural parameter of the auxiliary manifold in the previous round, σ represents the noise variance, tr represents the matrix trace operation, represents the second m-th projection of this round.

[0141] In addition, it can be understood that when this round is the first round, in S103, the statistical information of the previous round is the statistical information initialized in S101, the first-order average natural parameters of the auxiliary manifold of the previous round are the first-order average natural parameters of the auxiliary manifold initialized in S101, and the second-order average natural parameters of the auxiliary manifold of the previous round are the second-order average natural parameters of the auxiliary manifold initialized in S101.

[0142] S204: Determine the first-order average natural parameters of the auxiliary manifold of this round based on the first m projections of this round, the first-order average natural parameters of the auxiliary manifold of the previous round, and the damping value; determine the second-order average natural parameters of the auxiliary manifold of this round based on the second m projections of this round, the second-order average natural parameters of the auxiliary manifold of the previous round, and the damping value.

[0143] In some implementations of the present application, the first-order average natural parameter of the auxiliary manifold of this round is calculated by the following third formula (26):

[0144]

[0145] In the third formula, represents the first-order average natural parameter of the auxiliary manifold of this round, α represents the damping value, N represents the beam domain channel dimension, represents the first m projections of this round, represents the first-order average natural parameter of the auxiliary manifold in the previous round;

[0146] In some implementations of the embodiments of the present application, the second-order average natural parameter of this round is calculated using the following fourth formula:

[0147]

[0148] In the fourth formula (27), represents the second-order average natural parameter of this round, α represents the damping value, N represents the beam domain channel dimension, represents the first m projections of this round, represents the second-order mean natural parameter of the auxiliary manifold in the previous round.

[0149] S205: Determine the first-order natural parameters of the target manifold of this round based on the first-order average natural parameters of the auxiliary manifold of this round; determine the second-order natural parameters of the target manifold of this round based on the second-order average natural parameters of the auxiliary manifold of this round.

[0150] Among them, the first-order natural parameters of the target manifold describe the mean information of the channel state on the target manifold, that is, the global description of the channel parameters; the second-order natural parameters of the target manifold describe the variance information of the channel state on the target manifold, that is, the volatility of the channel state.

[0151] In some implementations of the present application, the first-order natural parameters of the target manifold of this round are calculated by the following fifth formula (28):

[0152]

[0153] In the fifth formula (28), represents the first-order natural parameter of the target manifold of this round, N represents the channel dimension in the beam domain, represents the first-order average natural parameter of the auxiliary manifold in the previous round;

[0154] In some implementations of the present application, the second-order natural parameters of the target manifold of this round are calculated by the following sixth formula (29):

[0155]

[0156] In the sixth formula (29), represents the second-order natural parameter of the target manifold of this round, N represents the channel dimension in the beam domain, represents the second-order mean natural parameter of the auxiliary manifold in the previous round.

[0157] S206: Determine the statistical information of this round based on the statistical information of the previous round, the first-order natural parameters of the target manifold of this round, and the second-order natural parameters of the target manifold of this round, and execute S202.

[0158] In some implementations of the embodiments of the present application, the statistical information of this round is calculated by the following seventh formula (30):

[0159]

[0160] In the seventh formula (30), D t+1 Represents the statistical information of this round, I represents the unit matrix, D t Indicates the statistics of the previous round. represents the second-order natural parameter of the target manifold of this round, Represents the first-order natural parameter of the target manifold of this round.

[0161] S207: Based on the first-order natural parameters of the target manifold in this round and the first-order natural parameters of the target manifold in the previous round, determine the change rate of the first-order parameters of the target manifold; based on the second-order natural parameters of the target manifold in this round and the second-order natural parameters of the target manifold in the previous round, determine the change rate of the second-order parameters of the target manifold; based on the statistical information of this round and the statistical information of the previous round, determine the change rate of the statistical information.

[0162] In an embodiment of the present application, the target manifold natural parameters specifically include: the first-order natural parameters of the target manifold and the second-order natural parameters of the target manifold. Therefore, determining the change rate of the target manifold parameters specifically includes determining the change rate of the first-order natural parameters of the target manifold and the change rate of the second-order natural parameters of the target manifold.

[0163] S208: Determine the mean of the target manifold according to the first-order natural parameters of the target manifold in the last round, the second-order natural parameters of the target manifold in the last round, and the statistical information in the last round, and use the mean of the target manifold as the beam domain channel evaluation result.

[0164] In some implementations of the embodiments of the present application, the mean of the target manifold is calculated by the following eighth formula (31):

[0165]

[0166] In the eighth formula (31), represents the mean of the target manifold, D T Indicates the statistics of the last round, represents the second-order natural parameter of the target manifold of this round, Represents the first-order natural parameter of the target manifold of this round.

[0167] The beam-domain channel assessment result is the channel state information estimated in the beam domain, which is described in this application as the mean of the target manifold. Compared with the beam domain, the space-frequency domain further incorporates the frequency dimension, so the space-frequency domain channel estimation result refers to the channel state information estimated in the spatial and frequency dimensions. By mapping the channel state in the beam space back to the combined space and frequency space, this application can better adapt to large-scale MIMO-OFDM systems and achieve more accurate channel state feedback.

[0168] exist Figure 2 In the process shown, there is no need to know the beam domain statistics in advance. Instead, the statistical information is updated during the iterative loop by updating the natural parameters of the target manifold. Updating the natural parameters of the target manifold only involves vector and scalar multiplication operations, so the computational complexity is low. It can be seen that the complexity of the technical solution provided by this application is not significantly improved compared to the existing technology. It solves the problem of the SIG algorithm requiring advance knowledge of statistical information without increasing the algorithm complexity, significantly improving the practicality of the SIG algorithm. At the same time, the final channel assessment result has higher accuracy.

[0169] Below, the performance of the embodiments of the present application is described based on actual application scenarios.

[0170] In actual application scenarios, the number of subcarriers N is taken p=300, cyclic prefix N g =72, N c = 1024, the base station is equipped with a 64×1 linear array, and the number of users K = 48. The test channel is the CDL-E channel.

[0171] When performing channel simulation, the normalized mean square error is defined as:

[0172]

[0173] Among them, G k is the spatial frequency domain channel of user k, is the estimated spatial frequency domain channel of user k.

[0174] See also Figure 3 , which shows the comparison between the performance of the method provided by the present invention (abbreviated as EM-SIG in the figure) and the original SIG performance, Figure 3 The PMKL-SIG statistics in the figure are calculated using a KL divergence-based statistics estimation algorithm, followed by channel estimation using the SIG algorithm. For both PMKL-12 and PMKL-48 in the illustration, an SNR of 30dB is assumed for statistical estimation, but the user grouping and estimation methods differ. Specifically, PMKL-12 represents a grouping strategy in which 12 users, using orthogonal pilots, are grouped together for joint statistical estimation. PMKL-48, on the other hand, directly estimates the statistics for 48 users. As can be seen from the figure, the performance of the proposed method is significantly better than that of a scheme that simultaneously estimates statistics for 48 users. Beyond this, the performance of the proposed method is only lower than that of the PMKL-12 scheme at low signal-to-noise ratios. However, since the PMKL-12 scheme requires 12 users to be grouped for statistical estimation, it consumes more resources and is significantly more complex than the EM-SIG algorithm.

[0175] See also Figure 4 The statistical information diagram estimated by the method of the present invention is shown. Figure 4 This is the statistical information for one user estimated by the method of the present invention. The figure shows that the statistical information estimated by the method of the present invention is sparse. There are 104 paths with energy above -40dB, 54 paths with energy above -30dB, 15 paths with energy above -20dB, and 4 paths with energy above -10dB.

[0176] See also Figure 5 The accuracy comparison chart of the statistical information estimation method of the present invention is shown in FIG. Figure 5As shown, the EM-SIG line represents the statistical information estimated using the EM-SIG algorithm, and the PMKL-12 line represents the statistical information estimated using the SIG algorithm. As can be seen from the figure, the performance of the statistical information estimated by the method of the present invention is higher than the statistical information estimated by PMKL-12. In other words, the present application has higher evaluation performance than the existing technologies mentioned in the background technology.

[0177] See also Figure 6 As shown, an embodiment of the present application further provides a channel assessment device, which includes:

[0178] Initialization module 601, used to initialize the auxiliary manifold natural parameters and statistical information, and set the damping value;

[0179] The loop execution module 602 is configured to execute the following loop steps until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold: determining the m-projection of the current round based on the statistical information of the previous round and the auxiliary manifold natural parameters of the previous round; determining the auxiliary manifold natural parameters of the current round based on the m-projection of the current round, the auxiliary manifold natural parameters of the previous round, and the damping value; determining the target manifold natural parameters of the current round based on the auxiliary manifold natural parameters of the current round; determining the statistical information of the current round based on the statistical information of the previous round and the target manifold natural parameters of the current round; determining the target manifold parameter change rate based on the target manifold natural parameters of the current round and the target manifold natural parameters of the previous round; and determining the statistical information change rate based on the statistical information of the current round and the statistical information of the previous round.

[0180] The channel evaluation module 603 is used to determine the mean of the target manifold based on the natural parameters of the target manifold of the last round and the statistical information of the last round in response to the target manifold parameter change rate or the statistical information change rate being less than a preset threshold, and use the mean of the target manifold as the beam domain channel evaluation result.

[0181] In some implementations of the embodiments of the present application, the apparatus further includes:

[0182] The result conversion module is used to convert the beam domain channel estimation result into the space-frequency domain channel estimation result.

[0183] In some implementations of the embodiments of the present application, the auxiliary manifold natural parameter includes: the auxiliary manifold first-order average natural parameter and the auxiliary manifold second-order average natural parameter;

[0184] Based on the statistical information of the previous round and the natural parameters of the auxiliary manifold of the previous round, the m-projection of this round is determined, including:

[0185] The first m-projection of this round is determined according to the statistical information of the previous round and the first-order average natural parameters of the auxiliary manifold of the previous round; the second m-projection of this round is determined according to the statistical information of the previous round and the second-order average natural parameters of the auxiliary manifold of the previous round.

[0186] In some implementations of the embodiments of the present application, determining the auxiliary manifold natural parameters of the current round according to the m-projection of the current round, the auxiliary manifold natural parameters of the previous round, and the damping value includes:

[0187] The first-order average natural parameters of the auxiliary manifold of this round are determined based on the first m projections of this round, the first-order average natural parameters of the auxiliary manifold of the previous round, and the damping value; the second-order average natural parameters of the auxiliary manifold of this round are determined based on the second m projections of this round, the second-order average natural parameters of the auxiliary manifold of the previous round, and the damping value.

[0188] In some implementations of the embodiments of the present application, the target manifold natural parameters include: first-order natural parameters of the target manifold and second-order natural parameters of the target manifold;

[0189] According to the natural parameters of the auxiliary manifold of this round, the natural parameters of the target manifold of this round are determined, including:

[0190] The first-order natural parameters of the target manifold of this round are determined based on the first-order average natural parameters of the auxiliary manifold of this round; the second-order natural parameters of the target manifold of this round are determined based on the second-order average natural parameters of the auxiliary manifold of this round;

[0191] In some implementations of the embodiments of the present application, determining the statistical information of the current round based on the statistical information of the previous round and the natural parameters of the target manifold of the current round includes:

[0192] The statistical information of this round is determined according to the statistical information of the previous round, the first-order natural parameters of the target manifold of this round, and the second-order natural parameters of the target manifold of this round.

[0193] In some implementations of the embodiments of the present application, determining the mean of the target manifold according to the natural parameters of the target manifold in the last round and the statistical information in the last round includes:

[0194] The mean of the target manifold is determined according to the first-order natural parameters of the target manifold in the last round, the second-order natural parameters of the target manifold in the last round, and the statistical information in the last round.

[0195] like Figure 7 As shown, the embodiment of the present application further provides a channel assessment device, including: a memory 701, a processor 702;

[0196] The memory 701 is used to store programs; the processor 702 is used to execute the programs in the memory to implement the various steps of the channel assessment method provided in the embodiment of the present application.

[0197] Furthermore, the fourth aspect of the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the channel assessment methods in the first aspect.

[0198] An embodiment of the present application further provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various steps of the channel assessment method provided in the embodiment of the present application are implemented.

[0199] Finally, it should be noted that, in the embodiments of the present application, relational terms such as first and second, etc., are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0200] The above description of the disclosed embodiments will enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A channel assessment method, characterized in that: The method comprises: Initialize the auxiliary manifold natural parameters and statistical information, and set the damping value; Execute the following loop steps until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold; Determine the m-projection of this round based on the statistical information of the previous round and the natural parameters of the auxiliary manifold of the previous round; Determining the auxiliary manifold natural parameters of this round according to the m-projection of this round, the auxiliary manifold natural parameters of the previous round, and the damping value; Determining the target manifold natural parameters of this round according to the auxiliary manifold natural parameters of this round; Determining statistical information of this round based on the statistical information of the previous round and the natural parameters of the target manifold of this round; Determining a target manifold parameter change rate based on the target manifold natural parameters of the current round and the target manifold natural parameters of the previous round; determining the statistical information change rate based on the statistical information of the current round and the statistical information of the previous round; In response to the target manifold parameter change rate or the statistical information change rate being less than the preset threshold, the mean of the target manifold is determined based on the natural parameters of the target manifold of the last round and the statistical information of the last round, and the mean of the target manifold is used as the beam domain channel evaluation result.

2. The method according to claim 1, characterized in that The method further comprises: The beam-domain channel estimation result is converted into a space-frequency-domain channel estimation result.

3. The method according to claim 1, characterized in that The auxiliary manifold natural parameters include: the auxiliary manifold first-order average natural parameters and the auxiliary manifold second-order average natural parameters; The step of determining the m-projection of the current round based on the statistical information of the previous round and the natural parameters of the auxiliary manifold of the previous round includes: The first m projections of this round are determined according to the statistical information of the previous round and the first-order average natural parameters of the auxiliary manifold of the previous round; the second m projections of this round are determined according to the statistical information of the previous round and the second-order average natural parameters of the auxiliary manifold of the previous round.

4. The method according to claim 3, characterized in that The determining of the auxiliary manifold natural parameters of the current round according to the m-projection of the current round, the auxiliary manifold natural parameters of the previous round, and the damping value includes: Determine the first-order average natural parameter of the auxiliary manifold of this round according to the first m projections of the current round, the first-order average natural parameter of the auxiliary manifold of the previous round, and the damping value; The second-order average natural parameters of the auxiliary manifold of the current round are determined according to the second m-projection of the current round, the second-order average natural parameters of the auxiliary manifold of the previous round, and the damping value.

5. The method according to claim 4, characterized in that The target manifold natural parameters include: target manifold first-order natural parameters and target manifold second-order natural parameters; Determining the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round includes: The first-order natural parameters of the target manifold of this round are determined according to the first-order average natural parameters of the auxiliary manifold of this round; the second-order natural parameters of the target manifold of this round are determined according to the second-order average natural parameters of the auxiliary manifold of this round.

6. The method according to claim 5, characterized in that Determining the statistical information of this round based on the statistical information of the previous round and the natural parameters of the target manifold of this round includes: The statistical information of this round is determined according to the statistical information of the previous round, the first-order natural parameters of the target manifold of this round, and the second-order natural parameters of the target manifold of this round.

7. The method according to claim 6, characterized in that Determining the mean of the target manifold according to the natural parameters of the target manifold in the last round and the statistical information in the last round includes: The mean of the target manifold is determined according to the first-order natural parameters of the target manifold in the last round, the second-order natural parameters of the target manifold in the last round, and the statistical information in the last round.

8. A channel assessment device, characterized in that: The device comprises: Initialization module, used to initialize the natural parameters and statistical information of the auxiliary manifold and set the damping value; a loop execution module, configured to execute the following loop steps until the target manifold parameter change rate or the statistical information change rate is less than a preset threshold: determining the m-projection of this round based on the statistical information of the previous round and the auxiliary manifold natural parameters of the previous round; determining the auxiliary manifold natural parameters of this round based on the m-projection of this round, the auxiliary manifold natural parameters of the previous round and the damping value; determining the target manifold natural parameters of this round based on the auxiliary manifold natural parameters of this round; determining the statistical information of this round based on the statistical information of the previous round and the target manifold natural parameters of this round; determining the target manifold parameter change rate based on the target manifold natural parameters of this round and the target manifold natural parameters of the previous round; determining the statistical information change rate based on the statistical information of this round and the statistical information of the previous round; A channel evaluation module is used to determine the mean of the target manifold in response to the target manifold parameter change rate or the statistical information change rate being less than the preset threshold, based on the target manifold natural parameters of the last round and the statistical information of the last round, and use the mean of the target manifold as the beam domain channel evaluation result.

9. A channel assessment device, characterized in that: The device includes a memory and a processor, and the processor is used to execute a program stored in the memory and run the channel assessment method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the channel estimation method according to any one of claims 1 to 7 are implemented.

11. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the channel assessment method according to any one of claims 1 to 7 is implemented.

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