Perception-assisted downlink CSI (Channel State Information) recovery method and device

Abstract

The invention discloses a perception-assisted downlink CSI (Channel State Information) recovery method and a perception-assisted downlink CSI recovery device. The perception-assisted downlink CSI recovery device comprises a downlink CSI initial estimation module, a false path suppression module and a lost path finding module. The BS end maps a perception support set according to the perceived flag matrix, and obtains initial estimation of a downlink CSI vector non-zero element according to the perception support set; the BS obtains a DD domain downlink CSI vector based on false path suppression according to the initialized downlink CSI vector; according to the DD domain downlink CSI vector, a more accurate downlink CSI recovery vector is retrieved based on the lost path. According to the method, the problem of unknown channel sparseness in compressed sensing is solved, and the calculation complexity of CSI reconstruction is reduced; compared with a classical CSI recovery method based on compressed sensing, the method provided by the invention has the advantages that the channel path information in the sensing signal is extracted to assist communication, and the recovery precision of the feedback CSI is greatly improved with relatively low resource consumption.

Description

Technical Field

[0001] The present invention relates to a perception-assisted downlink CSI recovery method and device, belonging to the field of communication technology, and is a channel state information (CSI) feedback technology for wireless communication systems such as 6G and WIFI 7 with massive multiple-input and multiple-output (mMIMO) technology in a frequency division duplex (FDD) mode. Background Art

[0002] mMIMO has become a promising technology to meet the growing demand for high data rates and spectral efficiency in advanced wireless communication systems such as 6G and WIFI 7. To fully exploit the advantages of mMIMO systems, it is crucial to obtain accurate downlink CSI at the base station (BS). In time-division duplex mMIMO systems, the downlink CSI can be estimated from the uplink CSI by exploiting the bidirectional channel reciprocity. However, due to the difference in downlink and uplink frequency bands, this channel reciprocity is usually weak in FDD mMIMO systems, resulting in the need to estimate the downlink CSI at the user equipment (UE) and feed it back to the BS. However, in mMIMO systems, the large number of antennas at the BS leads to huge feedback overhead, which significantly occupies the uplink transmission resources. In order to reduce the feedback overhead, CSI feedback based on compressed sensing (CS) has been developed by exploiting the sparsity of downlink CSI. However, CS-based CSI recovery methods heavily rely on the known channel sparsity, which is difficult to obtain in practical applications. In addition, CS-based CSI recovery methods usually use iterative reconstruction processing, resulting in very high computational complexity.

[0003] In recent years, integrated sensing and communication (ISAC) technology has become a key technology in the upcoming 6G era. By leveraging available sensor data, ISAC technology enhances the performance of communication-centric applications and promotes the development of perception-assisted communication. Many applications have been promoted based on this technology, such as perception-assisted beam training, perception-assisted beam tracking and prediction, and perception-assisted channel estimation. Based on this, we can use the support set obtained through perception to assist CSI recovery, thereby solving the problem of unknown channel coefficients and reducing the complexity of reconstruction calculations and achieving higher accuracy. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a perception-assisted downlink CSI recovery method and device for wireless communication systems such as 6G and WIFI 7. The present invention solves the problem of unknown channel sparsity in compressed sensing and reduces the computational complexity of CSI reconstruction. Compared with the classic CSI recovery method based on compressed sensing, the present invention assists communication by extracting channel path information from the perception signal, greatly improves the recovery accuracy of feedback CSI with less resource consumption.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A sensing-assisted downlink CSI recovery method comprises the following steps:

[0007] S1, nth t (n t =1,2,…,N t ) The root BS receiving antennas sense the marker matrix Mapping the perceptual support set And according to Get downlink CSI vector Initial estimate for the nonzero elements.

[0008] Among them, N t represents the number of receiving antennas of BS, Φ represents the measurement matrix, represents the pseudo-inverse operation, Indicates taking the matrix or vector a submatrix or subvector of indices, Indicates that according to the nth t The downlink CSI vector recovered without compression from the root transmit antenna;

[0009] The perceived sign matrix It refers to the matrix of channel path position information in the DD domain obtained by the BS based on the sensed echo signal (the path position in the matrix is ​​marked as 1, and the non-path position is marked as 0);

[0010] The channel equalization is performed by the BS end according to the received CSI feedback signal, and the specific algorithms include the least squares algorithm and the minimum mean square error algorithm;

[0011] S2, according to the initialized downlink CSI vector Based on false path suppression

[0012] The false path suppression refers to the perceived support set The index has the wrong channel path;

[0013] S3. According to Obtain more accurate downlink CSI recovery vector based on lost path recovery

[0014] The lost path retrieval refers to the perceived support set Index the channel paths where there are omissions.

[0015] Furthermore, the perception support set described in step S1 Mapping consists of the following sub-steps:

[0016] S1.1. Based on the sign matrix perceived by the BS The mapping matrix is ​​obtained using the following formula That is, the matrix Each element in Calculated as:

[0017]

[0018] Wherein, k = 1, 2, ..., M, l = 1, 2, ..., N, M represents the number of subcarriers of OTFS, and N represents the number of carrier symbols of OTFS;

[0019] S1.2, map the marker matrix Convert to map flag vector And use the following formula to obtain the perceptual support set

[0020]

[0021] Where j = 1, 2, ..., NM, express The jth element in ;

[0022] The mapping marker matrix Convert to map flag vector is to use Obtained, where vec(·) represents the column-by-column conversion operation from matrix to vector.

[0023] Specifically, the process of suppressing the false path described in step S2 includes:

[0024] S2.1, Initialization processing: According to the initial estimate Downlink CSI vector Initialize to The residual vector r0, the iteration counter i and the support set Ω (0) are initialized to i=1 and

[0025] S2.2. Based on the measurement matrix Φ and the residual vector r0, calculate the Pearson correlation coefficient vector ρ, whose qth element ρ q Calculated as:

[0026]

[0027] in, express The number of elements in , Represents vector The qth element in N a Represents the compressed downlink CSI vector Length;

[0028] S2.3. False path suppression is performed based on the Pearson correlation coefficient vector ρ to obtain the DD domain downlink CSI vector Its elements Calculated as:

[0029]

[0030] Among them, |·| represents the operation of taking the modulus value of the complex value, T h It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

[0031] Furthermore, the lost path retrieval described in step S3 includes the following sub-steps:

[0032] S3.1. Downlink CSI vector after false path suppression The measurement matrix Φ and the initial residual vector r0 are initialized to i = 1, and the maximum number of iterations is Update the residual vector r i for:

[0033]

[0034] S3.2, according to the measurement matrix Φ and the residual vector r i , calculate the Pearson correlation coefficient vector ρ i , whose jth element Calculated as:

[0035]

[0036] Where j = 1, 2, ..., NM,

[0037] S3.3, according to the Pearson correlation coefficient vector ρ i , use the following formula to obtain the index λ of the maximum correlation coefficient i for:

[0038]

[0039] S3.4. Index λ according to the maximum correlation coefficient i , use the following formula to update the support set Ω (i) :

[0040] Ω (i) =Ω (i-1) ∪λ i

[0041] S3.5. According to the support set Ω (i) and the residual vector r i , use the following formula to calculate the downlink CSI vector

[0042]

[0043] S3.6, according to the downlink CSI vector and the residual vector r i , use the following formula to update the residual vector r for the next iteration i+1 :

[0044]

[0045] S3.7, update iteration counter i=i+1; if Return to step S3.2; otherwise,

[0046]

[0047] End the iterative process;

[0048] in, It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

[0049] The perception-assisted downlink CSI recovery device provided by the present invention is used to implement the above-mentioned perception-assisted downlink CSI recovery method, including:

[0050] Downlink CSI initial estimation module, based on the perception support set Get the initial estimated downlink CSI vector

[0051] The false path suppression module, according to claim 3, is designed to eliminate to obtain the false path in

[0052] The lost path retrieval module is designed according to claim 4 to obtain a more accurate downlink CSI vector BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0054] Figure 2 It is a flow chart of the false path suppression module of the present invention;

[0055] Figure 3 This is a flow chart of the lost path retrieval module of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0057] Reference Figure 1 , a sensing-assisted downlink CSI recovery method includes:

[0058] S1, nth t (n t =1,2,…,N t ) The root BS receiving antennas sense the marker matrix Mapping the perceptual support set And according to Get downlink CSI vector Initial estimate for the nonzero elements.

[0059] Among them, N t represents the number of receiving antennas of BS, Φ represents the measurement matrix, represents the pseudo-inverse operation, Indicates taking the matrix or vector a submatrix or subvector of indices, Indicates that according to the nth t The downlink CSI vector recovered without compression from the root transmit antenna;

[0060] The perceived sign matrix It refers to the matrix of channel path position information in the DD domain obtained by the BS based on the sensed echo signal (the path position in the matrix is ​​marked as 1, and the non-path position is marked as 0);

[0061] The channel equalization is performed by the BS end according to the received CSI feedback signal, and the specific algorithms include the least squares algorithm and the minimum mean square error algorithm;

[0062] Among them, some more specific implementation methods are as follows:

[0063] The perception support set In the logo matrix The mapping mark matrix is ​​obtained by shifting the elements And use After vectorization, we get the mapping flag vector According to the expression Get, where j = 1, 2, ..., NM, express The jth element in ;

[0064] S2, according to the initialized downlink CSI vector Based on false path suppression

[0065] The false path suppression refers to the perceived support set The index has the wrong channel path;

[0066] S3. According to Obtain more accurate downlink CSI recovery vector based on lost path recovery

[0067] The lost path retrieval refers to the perceived support set Index the channel paths where there are omissions.

[0068] Reference Figure 2 , step S2 further includes the following sub-steps:

[0069] S2.1, Initialization processing: According to the initial estimate Downlink CSI vector Initialize to The residual vector r0, the iteration counter i and the support set Ω (0) are initialized to i=1 and

[0070] S2.2. Based on the measurement matrix Φ and the residual vector r0, calculate the Pearson correlation coefficient vector ρ, whose qth element ρ q Calculated as:

[0071]

[0072] in, express The number of elements in , Represents vector The qth element in N a Represents the compressed downlink CSI vector Length;

[0073] S2.3. False path suppression is performed based on the Pearson correlation coefficient vector ρ to obtain the DD domain downlink CSI vector Its elements Calculated as:

[0074]

[0075] Among them, |·| represents the operation of taking the modulus value of the complex value, T h It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

[0076] Reference Figure 3 , step S3 further includes the following sub-steps:

[0077] S3.1. Downlink CSI vector after false path suppression The measurement matrix Φ and the initial residual vector r0 are initialized to i = 1, and the maximum number of iterations is Update the residual vector r i for

[0078]

[0079] S3.2, according to the measurement matrix Φ and the residual vector r i , calculate the Pearson correlation coefficient vector ρ i , whose jth element Calculated as:

[0080]

[0081] Where j = 1, 2, ..., NM,

[0082] S3.3, according to the Pearson correlation coefficient vector ρ i , use the following formula to obtain the index λ of the maximum correlation coefficient i for:

[0083]

[0084] S3.4. Index λ according to the maximum correlation coefficient i , use the following formula to update the support set Ω (i) :

[0085] Ω (i) =Ω (i-1) ∪λ i

[0086] S3.5. According to the support set Ω (i) and the residual vector r i, use the following formula to calculate the downlink CSI vector

[0087]

[0088] S3.6, according to the downlink CSI vector and the residual vector r i , use the following formula to update the residual vector r for the next iteration i+1 :

[0089]

[0090] S3.7, update iteration counter i=i+1; if Return to step S3.2; otherwise,

[0091]

[0092] End the iterative process;

[0093] in, It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

[0094] Example 1

[0095] In step S1, the perception support set is obtained A specific embodiment is as follows:

[0096] Assumptions: M = 4, N = 4, the marker matrix for:

[0097]

[0098] Obtained mapping marker matrix for:

[0099]

[0100] Mapping marker matrix Vectorize, that is, use Get Map Flag Vector for:

[0101]

[0102] Under map sign vector Get the perceptual support set, that is The perceptual support set can be calculated for:

[0103]

[0104] Example 2

[0105] In step S2, the DD domain downlink CSI vector is obtained based on false path suppression A specific embodiment is as follows:

[0106] Assume: M = 2, N = 2, after initialization for:

[0107]

[0108] The measurement matrix Φ is:

[0109]

[0110] According to step S2.2, the Pearson correlation coefficient vector ρ is calculated as:

[0111] ρ = [0.1419 0 0 0];

[0112] Based on the false path suppression in step S2.3, the DD domain downlink CSI vector can be calculated. for:

[0113]

[0114] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A perception-assisted downlink CSI recovery method, characterized in that: The following steps are involved: S1, nth t The root BS receiving antennas detect the marker matrix Mapping the perceptual support set And according to Get downlink CSI vector initial estimate of non-zero elements; n t =1,2,...,N t ; Among them, N t represents the number of receiving antennas of BS, Φ represents the measurement matrix, represents the pseudo-inverse operation, Indicates taking the matrix or vector a submatrix or subvector of indices, Indicates that according to the nth t The downlink CSI vector recovered without compression from the root transmit antenna; The perceived sign matrix It refers to the matrix of channel path position information in the DD domain obtained by the BS based on the sensed echo signal; the path position in the matrix is ​​marked as 1, and the non-path position is marked as 0; The channel equalization is performed by the BS end according to the received CSI feedback signal, and the specific algorithms include the least squares algorithm and the minimum mean square error algorithm; S2, according to the initialized downlink CSI vector Based on false path suppression The false path suppression refers to the perceived support set The index has the wrong channel path; S3. According to Obtain more accurate downlink CSI recovery vector based on lost path recovery The lost path retrieval refers to the perceived support set Index the channel paths where there are omissions.

2. The method for downlink CSI recovery using sensing assistance according to claim 1, characterized in that: Perception support set in S1 Mapping consists of the following sub-steps: S1.

1. Based on the sign matrix perceived by the BS The mapping matrix is ​​obtained using the following formula That is, the matrix Each element in Calculated as: Wherein, k=1, 2, ..., M, l=1, 2, ..., N, M represents the number of subcarriers of OTFS, and N represents the number of carrier symbols of OTFS; S1.2, map the marker matrix Convert to map flag vector And use the following formula to obtain the perceptual support set Where j = 1, 2, ..., NM, express The jth element in ; The mapping marker matrix Convert to map flag vector is to use Obtained, where vec(·) represents the column-by-column conversion operation from matrix to vector.

3. The method for downlink CSI recovery with perception assistance according to claim 1, characterized in that: False path suppression in S2 includes the following sub-steps: S2.1, Initialization processing: According to the initial estimate Downlink CSI vector Initialize to The residual vector r0, the iteration counter i and the support set Ω (0) are initialized to i=1 and S2.

2. Based on the measurement matrix Φ and the residual vector r0, calculate the Pearson correlation coefficient vector ρ, whose qth element ρ q Calculated as: in, express The number of elements in , Represents vector The qth element in N a Represents the compressed downlink CSI vector Length; S2.

3. False path suppression is performed based on the Pearson correlation coefficient vector ρ to obtain the DD domain downlink CSI vector Its elements Calculated as: Among them, |·| represents the operation of taking the modulus value of the complex value, T h It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

4. The method for downlink CSI recovery with perception assistance according to claim 1, characterized in that: Retrieval of lost paths in S3 includes the following sub-steps: S3.

1. Downlink CSI vector after false path suppression The measurement matrix Φ and the initial residual vector r0 are initialized to i = 1, and the maximum number of iterations is Update the residual vector r i for: S3.2, according to the measurement matrix Φ and the residual vector r i , calculate the Pearson correlation coefficient vector ρ i , whose jth element Calculated as: Where j = 1, 2, ..., NM, S3.3, according to the Pearson correlation coefficient vector ρ i , use the following formula to obtain the index λ of the maximum correlation coefficient i for: S3.

4. Index λ according to the maximum correlation coefficient i , use the following formula to update the support set Ω (i) : Oh (i) =Oh (i-1) ∪λ i S3.

5. According to the support set Ω (i) and the residual vector r i , use the following formula to calculate the downlink CSI vector S3.6, according to the downlink CSI vector and the residual vector r i , use the following formula to update the residual vector r for the next iteration i+1 : S3.7, update iteration counter i=i+1; if Return to step S3.2; otherwise, End the iterative process; in, It represents the correlation coefficient threshold, which is designed based on actual engineering experience.

5. A perception-assisted downlink CSI recovery device, characterized in that: A method for realizing a sensing-assisted downlink CSI recovery method according to any one of claims 1 to 4, wherein the device comprises: The downlink CSI initial estimation module uses the perception support set Get the initial estimated downlink CSI vector The false path suppression module, according to claim 3, is designed to eliminate to obtain the false path in The lost path retrieval module is designed according to claim 4 to obtain a more accurate downlink CSI vector

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