Interference equalization method and device for multi-carrier modulation in high dynamic scene, equipment and storage medium

Abstract

The invention discloses an interference equalization method, device and equipment for multi-carrier modulation in a high-dynamic scene and a storage medium, and relates to the technical field of mobile communication, and the method comprises the steps: obtaining a time domain channel matrix of a time-frequency double-color dispersion channel, and determining an inversion core matrix in a minimum mean square error equalization matrix based on the time domain channel matrix; based on the sparsity structure of the inversion core matrix, decomposing the inversion core matrix to obtain a decomposition matrix; based on the time domain channel matrix, the inversion core matrix and the decomposition matrix, determining a multilayer linear equation set with minimum mean square error equalization; and solving the multi-layer linear equation set to obtain an equalization result. By means of the mode, the calculation complexity of the equalization process in the process of resisting inter-symbol interference and inter-carrier interference existing in multi-carrier modulation in a high dynamic scene is remarkably reduced, excellent performance is still kept under the extreme condition that no cyclic prefix exists or the length of the cyclic prefix is zero, and high scene adaptability and system practicability are embodied.

Description

Technical Field

[0001] This application relates to the field of mobile communication technology, and in particular to interference equalization methods, apparatus, devices and storage media for multi-carrier modulation in high dynamic scenarios. Background Technology

[0002] In highly dynamic wireless communication environments, equalization schemes can be used to combat the effects of inter-symbol interference (ISI) and inter-carrier interference (ICI) in multi-carrier modulation (orthogonal time-frequency-space and orthogonal frequency division multiplexing) to improve communication system performance. Traditional equalization schemes generally assume a biorthogonal condition, meaning no ISI or ICI. However, in real-world environments, the complex electromagnetic environment and extremely high relative speeds between transmitters and receivers lead to multipath and Doppler effects. Furthermore, as carrier frequencies shift to higher bands (e.g., 5G millimeter waves, 6G terahertz), the Doppler effect intensifies, and the channel exhibits time-frequency bidispersion characteristics. These drastic channel changes make communication stability difficult to guarantee, and the biorthogonal condition no longer holds. Since real-time dynamic compensation for ISI and ICI is necessary, receiver complexity increases dramatically. Therefore, a low-complexity equalization scheme for ISI and ICI in multi-carrier modulation is urgently needed.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide an interference equalization method, apparatus, device, and storage medium for multi-carrier modulation in high dynamic scenarios, aiming to solve the technical problem that the equalization schemes for resisting inter-symbol interference and inter-carrier interference in high dynamic scenarios are highly complex.

[0005] To achieve the above objectives, this application provides an interference equalization method for multi-carrier modulation in high dynamic scenarios, the method comprising: Obtain the time-domain channel matrix of the time-frequency dual-dispersion channel, and based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix; Based on the sparse structure of the inverse core matrix, the inverse core matrix is ​​decomposed to obtain the decomposed matrix; Based on the time-domain channel matrix, the inverse core matrix, and the decomposition matrix, a multi-level linear equation system with minimum mean square error equilibrium is determined. The equilibrium result is obtained by solving the system of multilevel linear equations.

[0006] In one embodiment, the decomposition matrix includes a lower triangular matrix, a real diagonal matrix, and the conjugate transpose of the lower triangular matrix; The step of decomposing the inverted core matrix based on its sparse structure to obtain the decomposed matrix includes: Based on the sparse structure of the inverse core matrix, the structure of the real diagonal matrix and the structure of the lower triangular matrix are determined. Calculate the non-zero elements of the real diagonal matrix column by column, and calculate the non-zero elements of the lower triangular matrix column by column; The real diagonal matrix is ​​determined based on the non-zero elements of the real diagonal matrix and the structure of the real diagonal matrix; The lower triangular matrix is ​​determined based on the non-zero elements of the lower triangular matrix and the structure of the lower triangular matrix; Based on the lower triangular matrix, determine the conjugate transpose of the lower triangular matrix.

[0007] In one embodiment, the step of calculating the non-zero elements of the lower triangular matrix column by column includes: The range of column index values ​​is determined based on the current symbol index value, the number of subcarriers, and the number of symbols. When the current column index value matches the range of the column index value, the range of the row index value corresponding to the current column index value is determined based on the matching value range of the current column index value; Based on the time-domain channel matrix and the real diagonal matrix, calculate the non-zero elements of the lower triangular matrix within the range of the row index values ​​corresponding to the current column index value.

[0008] In one embodiment, the step of determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value includes: When the matching value range of the current column index value is the value range of the first column, the lower limit of the first row index value is determined based on the current column index value and a preset constant, and the upper limit of the first row index value is determined based on the current column index value, the number of channel delay paths, and the preset constant. The range of the first row index value is determined based on the lower limit and the upper limit of the first row index value; The lower limit of the second row index value is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the second row index value is determined based on the upper limit of the column index value range. The range of the second row index value is determined based on the lower limit and the upper limit of the second row index value; The range of index values ​​in the first row and the range of index values ​​in the second row are used as the range of row index values ​​corresponding to the current column index value.

[0009] In one embodiment, the step of determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value includes: When the matching value range of the current column index value is the value range of the second column, the lower limit of the third row index value is determined based on the current column index value and a preset constant, and the upper limit of the third row index value is determined based on the upper limit of the column index value range; The range of the third row index value is determined based on the lower limit and the upper limit of the third row index value. Use the range of index values ​​in the third row as the range of row index values ​​corresponding to the current column index value.

[0010] In one embodiment, the step of determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value includes: When the matching value range of the current column index value is the value range of the third column, the lower limit of the fourth row index value is determined based on the upper limit of the column index value range and a preset constant, and the upper limit of the fourth row index value is determined based on the current column index value, the cyclic prefix length, the number of channel delay paths and the preset constant. The range of the fourth row index value is determined based on the lower limit and the upper limit of the fourth row index value. The lower limit of the index value of the fifth row is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the index value of the fifth row is determined based on the upper limit of the column index value range. The range of the fifth row index value is determined based on the lower limit and the upper limit of the fifth row index value. The range of index values ​​in the third row, the range of index values ​​in the fourth row, and the range of index values ​​in the fifth row are used as the range of row index values ​​corresponding to the current column index value.

[0011] In one embodiment, the step of solving the multilevel linear equation system to obtain the equilibrium result includes: Based on the sparse structure of the conjugate transpose of the time-domain channel matrix, the product of the conjugate transpose of the time-domain channel matrix and the received signal vector is calculated to obtain the right-hand vector. Based on the sparse structure of the lower triangular matrix, the first equation between the lower triangular matrix, the first intermediate result vector, and the right-hand vector is solved forward to obtain the first intermediate result vector. Based on the sparse structure of the real diagonal matrix, the first intermediate result vector is diagonally scaled to obtain the second intermediate result vector; Based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, the second equation between the conjugate transpose of the lower triangular matrix, the equalization result, and the second intermediate result vector is solved backward to obtain the equalization result.

[0012] Furthermore, to achieve the above objectives, this application also proposes an interference equalization device for multi-carrier modulation in high dynamic scenarios. The interference equalization device for multi-carrier modulation in high dynamic scenarios includes: The inverse characterization module is used to obtain the time-domain channel matrix of the time-frequency dual-dispersion channel, and based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix. The matrix decomposition module is used to decompose the inverse core matrix based on the sparse structure of the inverse core matrix to obtain the decomposed matrix; The equalization transformation module is used to determine the multi-level linear equation system with minimum mean square error equalization based on the time domain channel matrix, the inverse core matrix, and the decomposition matrix. The equation solving module is used to solve the multi-level linear equation system and obtain the equilibrium result.

[0013] Furthermore, to achieve the above objectives, this application also proposes an interference equalization device for multi-carrier modulation in high dynamic scenarios. The interference equalization device for multi-carrier modulation in high dynamic scenarios includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described above.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described above.

[0016] This application provides an interference equalization method for multi-carrier modulation in high-dynamic scenarios. It obtains the time-domain channel matrix of a time-frequency bidispersive channel, and based on the time-domain channel matrix, determines the inverse core matrix in the minimum mean square error equalization matrix. Based on the sparsity structure of the inverse core matrix, it decomposes the inverse core matrix to obtain the decomposed matrix. Based on the time-domain channel matrix, the inverse core matrix, and the decomposed matrix, a multi-level linear equation system for minimum mean square error equalization is determined. The multi-level linear equation system is solved to obtain the equalization result. This application can significantly reduce the computational complexity of the equalization process for resisting inter-symbol interference and inter-carrier interference in multi-carrier modulation under high-dynamic scenarios. It maintains excellent performance even under extreme conditions with zero cyclic prefix length, demonstrating strong scenario adaptability and system practicality. It solves the technical problem of high complexity in equalization schemes for resisting inter-symbol interference and inter-carrier interference in multi-carrier modulation under high-dynamic scenarios. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the interference equalization method for multi-carrier modulation in high-dynamic scenarios according to this application. Figure 2 This is a schematic diagram of the time-domain channel matrix for the interference equalization method of multi-carrier modulation in high dynamic scenarios provided in Embodiment 1 of this application; Figure 3 A schematic diagram of the inverse core matrix of the interference equalization method for multi-carrier modulation in high dynamic scenarios provided in Embodiment 1 of this application; Figure 4 This is a schematic diagram of the lower triangular matrix of the interference equalization method for multi-carrier modulation in high dynamic scenarios provided in Embodiment 1 of this application; Figure 5 This is a flowchart illustrating Embodiment 2 of the interference equalization method for multi-carrier modulation in high-dynamic scenarios according to this application. Figure 6 This is a simplified flowchart illustrating the interference equalization method for multi-carrier modulation in a high-dynamic scenario provided in Embodiment 2 of this application. Figure 7 This is a schematic diagram of the module structure of the interference equalization device for multi-carrier modulation in a high dynamic scene according to an embodiment of this application; Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the interference equalization method of multi-carrier modulation in high dynamic scenarios in the embodiments of this application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: obtain the time-domain channel matrix of the time-frequency bidispersive channel; based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix; based on the sparsity structure of the inverse core matrix, decompose the inverse core matrix to obtain the decomposed matrix; based on the time-domain channel matrix, the inverse core matrix, and the decomposed matrix, determine the multi-level linear equation system of minimum mean square error equalization; solve the multi-level linear equation system to obtain the equalization result.

[0024] Currently, existing equalization schemes still have some problems, such as: high complexity; compensation accuracy decreases with iteration; and they are only applicable when the cyclic prefix (CP) is greater than the number of channel delay paths, i.e., they only consider inter-carrier interference and not inter-symbol interference.

[0025] This application provides a solution that can significantly reduce the computational complexity of the equalization process for resisting inter-symbol interference and inter-carrier interference in high-dynamic scenarios. It maintains excellent performance even under extreme conditions where the cyclic prefix length is zero, demonstrating strong scenario adaptability and system practicality. It solves the technical problem of high complexity in equalization schemes for resisting inter-symbol interference and inter-carrier interference in high-dynamic scenarios.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, or an interference equalization device for multi-carrier modulation in high dynamic scenarios, etc. This embodiment does not specifically limit it. The following uses an interference equalization device for multi-carrier modulation in high dynamic scenarios as an example to describe this embodiment and the following embodiments.

[0027] This application provides an interference equalization method for multi-carrier modulation in high dynamic scenarios, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the interference equalization method for multi-carrier modulation in high-dynamic scenarios according to this application.

[0028] In this embodiment, the interference equalization method for multi-carrier modulation in high dynamic scenarios includes steps S10~S40: Step S10: Obtain the time-domain channel matrix of the time-frequency dual-dispersion channel, and determine the inverse core matrix in the minimum mean square error equalization matrix based on the time-domain channel matrix. It should be noted that this embodiment applies to multi-carrier modulation in high-dynamic scenarios, where multi-carrier modulation includes orthogonal time-frequency space-time modulation and orthogonal frequency division multiplexing. In actual wireless communication environments, due to the complex electromagnetic environment and extremely high relative movement speed between the transmitter and receiver, multipath effects and Doppler effects occur. At the same time, as the carrier frequency moves to higher frequency bands (e.g., 5G millimeter wave, 6G terahertz, etc.), the Doppler effect intensifies, and the channel will have time-frequency bidispersive characteristics. In this case, the channel can be considered a time-frequency bidispersive channel.

[0029] It is understandable that this embodiment can accurately characterize the time-domain channel matrix. The structure.

[0030] In one feasible implementation, the step of determining the inverse core matrix in the minimum mean square error equalization matrix based on the time-domain channel matrix includes: obtaining the correspondence between the time-domain channel matrix, the conjugate transpose of the time-domain channel matrix, the noise variance of the received signal vector, the identity matrix, and the minimum mean square error equalization matrix; and extracting the inverse core matrix in the minimum mean square error equalization matrix based on the correspondence.

[0031] It should be noted that the minimum mean square error (MMSE) balancing process can be expressed as: ,in, To achieve the desired equilibrium outcome, For the received signal vector, It is the minimum mean square error equilibrium matrix, i.e., the MMSE equilibrium matrix.

[0032] It is understandable that the correspondence between the time-domain channel matrix, the conjugate transpose of the time-domain channel matrix, the noise variance of the received signal vector, the identity matrix, and the minimum mean square error equalization matrix, i.e., the calculation formula for the minimum mean square error equalization matrix, is as follows:

[0033] In the formula, This is the minimum mean square error equilibrium matrix. The time-domain channel matrix, This indicates taking the conjugate transpose of the matrix. It is the conjugate transpose of the time-domain channel matrix. This represents the noise variance (noise power) of the received signal vector. It is an identity matrix.

[0034] It should be understood that the minimum mean square error equilibrium matrix The calculation involves large-scale matrix inversion. This embodiment utilizes the structure of the time-domain channel matrix to characterize the minimum mean square error equalization matrix. The part that needs to be inverted, i.e., the inverse of the core matrix. , .

[0035] In practical implementation, when the number of channel delay paths Greater than the length of the cyclic prefix At that time, the discrete-time domain channel matrix The structure can be referenced. Figure 2 , Figure 2 middle For the number of subcarriers, Let be the number of signs. For the discrete-time input-output relationship, the MMSE equalization matrix is ​​represented as: The part of the MMSE equilibrium matrix that needs to be inverted is defined as follows: Based on the structure of the discrete-time domain channel, we can obtain the following: Figure 3 shown Matrix structure.

[0036] Step S20: Based on the sparse structure of the inverse core matrix, the inverse core matrix is ​​decomposed to obtain the decomposed matrix; It should be noted that the decomposition in this embodiment refers to... Decomposition, the decomposition matrix is ​​obtained by... The resulting matrices include lower triangular matrices (unit lower triangular matrices). Real diagonal matrix and the conjugate transpose of the lower triangular matrix .

[0037] It is understandable that, based on the inverse core matrix... The sparsity of it Decomposition can be used to find the inverse core matrix. Decomposed into a lower triangular matrix Real diagonal matrix and the conjugate transpose of the lower triangular matrix .

[0038] It should be understood that, due to the inverse of the core matrix... The structure of the lower triangular matrix is ​​known, therefore... and real diagonal matrix The structure is known; in the specific implementation, only the lower triangular matrix needs to be calculated. and real diagonal matrix The non-zero elements.

[0039] In specific implementation, conduct Decompose it to make full use of its sparsity. Because It is a Hermitian matrix, which is always a positive semi-definite matrix. Furthermore, for a finite range of SNR, we have... ,so It must be a positive definite matrix and invertible, thus allowing us to... conduct Decomposition. Among them, It is a unit lower triangular matrix with its main diagonal elements being 1. It is a real diagonal matrix. Decomposition requires calculating the matrix column by column. diagonal elements sum matrix off-diagonal elements .

[0040] For the Columns, calculating matrices diagonal elements :

[0041] Calculate matrix off-diagonal elements :

[0042] In the formula, when When fixed, The first non-zero value depends on Meanwhile, due to calculation The iterative process leads to the first The column following this position is calculated All are non-zero, therefore, the matrix The structure can be referenced. Figure 4 .

[0043] Step S30: Based on the time-domain channel matrix, the inverse core matrix, and the decomposition matrix, determine the multilevel linear equation system with minimum mean square error equilibrium. It should be noted that this embodiment transforms the MMSE equilibration process into a solution process for a multilevel linear equation system, as shown below:

[0044] In the formula, The time-domain channel matrix, It is the conjugate transpose of the time-domain channel matrix. This represents the noise variance (noise power) of the received signal vector. For the identity matrix, To achieve a balanced outcome, For the received signal vector, It is a lower triangular matrix. It is a real diagonal matrix. It is the conjugate transpose of a lower triangular matrix.

[0045] Step S40: Solve the multi-level linear equation system to obtain the equilibrium result.

[0046] It should be noted that solving the multilevel linear equation system yields the final equilibrium result. .

[0047] In one feasible implementation, step S40 may include steps S401 to S404: Step S401: Based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, calculate the product of the conjugate transpose of the time-domain channel matrix and the received signal vector to obtain the right-hand vector. It should be noted that the right-hand vector is the preprocessed intermediate vector, which serves as the "right-hand input" for solving the subsequent system of equations. The right-hand vector is the product of the conjugate transpose of the time-domain channel matrix and the received signal vector.

[0048] It is understandable that the time-domain channel matrix is ​​used. To assess sparsity, the right-hand vector is calculated as follows:

[0049] In the formula, The vector on the right side, It is the conjugate transpose of the time-domain channel matrix. This is the received signal vector. The computational complexity of this process is extremely low, involving only matrix-vector multiplication, and it utilizes the time-domain channel matrix. The sparsity of the element only requires the calculation of non-zero elements.

[0050] Step S402: Based on the sparse structure of the lower triangular matrix, solve the first equation between the lower triangular matrix, the first intermediate result vector, and the right-side vector to obtain the first intermediate result vector. It should be noted that the main diagonal elements of the lower triangular matrix are all 1s, and only the lower triangular region has non-zero elements, the positions of which are determined by the channel sparsity. The first intermediate result vector is the intermediate result obtained from the first recursion.

[0051] Additionally, it should be noted that the first equation relating the lower triangular matrix, the first intermediate result vector, and the right-hand vector is as follows:

[0052] In the formula, The vector on the right side, It is a lower triangular matrix. This is the first intermediate result vector.

[0053] Understandably, due to It is a lower triangular matrix, and the solution is obtained by forward recursion, that is, starting from the first element, calculating each element in turn. Subsequent element calculations can directly reuse the results obtained earlier, without needing to traverse the entire matrix (only non-zero elements are processed). The forward computation complexity of the lower triangular matrix is ​​only... ,in, The number of paths with channel delay. For the number of subcarriers, (where the number is a sign number), which is much lower than matrix inversion.

[0054] Step S403: Based on the sparse structure of the real diagonal matrix, the first intermediate result vector is diagonally scaled to obtain the second intermediate result vector; It should be noted that the real diagonal matrix has non-zero real numbers only on the main diagonal, with all other positions containing 0. The second intermediate result vector is the intermediate result obtained from the second recursion.

[0055] Additionally, it should be noted that the equation to be solved is:

[0056] In the formula, It is a real diagonal matrix. This is the first intermediate result vector. This is the second intermediate result vector.

[0057] Understandably, due to It is a real diagonal matrix. The essence of diagonal matrix operations is diagonal scaling, that is... Each element is equal to Divide the corresponding element by The corresponding elements on the main diagonal, i.e. The entire process involves element-wise operations, without complex matrix multiplication, resulting in extremely low complexity.

[0058] Step S404: Based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, solve the second equation between the conjugate transpose of the lower triangular matrix, the equalization result, and the second intermediate result vector to obtain the equalization result.

[0059] It should be noted that, due to It is a unit lower triangular matrix, and its conjugate transpose is a unit upper triangular matrix, i.e. Only the upper triangular region contains non-zero elements. The equalization result is the final MMSE equalization result vector, which is the signal vector after eliminating ISI and ICI interference.

[0060] Additionally, it should be noted that the second equation relating the conjugate transpose of the lower triangular matrix, the equilibrium result, and the second intermediate result vector is as follows:

[0061] In the formula, It is the conjugate transpose of a lower triangular matrix. To achieve a balanced outcome, This is the second intermediate result vector.

[0062] Understandably, due to It is an upper triangular matrix, and the solution is obtained by backward recursion, that is, starting from the last element, calculating each element in reverse order. Similarly, only non-zero elements are processed, and the previous calculation results are reused to finally obtain the equilibrium result. The complexity of the backward solution is the same as that of the forward solution.

[0063] In practical implementation, after MMSE equalization, the equalization process can be expressed as: . By applying decomposition, it is transformed into solving a system of multilevel linear equations, that is... .if Given a standard lower triangular matrix, the process of solving the linear equation is as follows: Calculate the right-hand vector: .

[0064] Forward solver: , , .

[0065] Diagonal scaling: , , .

[0066] Backward solution: , , .

[0067] The subscript values ​​only need to be taken at non-zero positions in the matrix. Due to the sparsity of the matrix, the balanced complexity will be greatly reduced.

[0068] It should be understood that the traditional MMSE equilibrium algorithm based on direct matrix inversion has a complexity level of O(n log n). The complexity is relatively high, but this embodiment reduces the complexity level to a lower level by decomposing the MMSE leveling process. This allows for the implementation of a low-complexity MMSE equalization algorithm. When the number of channel delay paths... Much smaller than the number of subcarriers and symbolic number At this time, the complexity is greatly reduced, and the same performance as the traditional MMSE equalization algorithm can be achieved.

[0069] In this embodiment, by analyzing the time-domain channel matrix... The matrix was analyzed, explored, and utilized. The sparse structure, which is the theoretical foundation for implementing low-complexity algorithms, is based on... The sparse structure was designed to achieve low complexity. Decomposition mechanism to explicitly obtain the lower triangular matrix With real diagonal matrix The sparse representation transforms the large-scale matrix inversion problem in traditional MMSE equilibrium into a recursively solvable multilevel linear equation system problem; it decomposes the equilibrium process into a system based on... , and The sparse matrix operation module, through structural coupling between modules and algorithmic collaboration, reduces the overall computational complexity from direct inversion. Reduce to This method achieves MMSE equalization with near-linear complexity; it fully considers inter-symbol interference and inter-carrier interference that occur in highly dynamic environments, does not rely on CP structure, and has better environmental robustness and system compatibility; even under the constraint of zero CP length, the scheme in this embodiment can still work effectively, providing a practically deployable low-complexity MMSE equalization scheme for future communication systems (such as 6G, CP-less waveforms).

[0070] This embodiment provides an interference equalization method for multi-carrier modulation in high-dynamic scenarios. It obtains the time-domain channel matrix of a time-frequency bidispersive channel, and based on the time-domain channel matrix, determines the inverse core matrix in the minimum mean square error equalization matrix. Based on the sparsity structure of the inverse core matrix, it decomposes the inverse core matrix to obtain the decomposed matrix. Based on the time-domain channel matrix, the inverse core matrix, and the decomposed matrix, a multi-level linear equation system for minimum mean square error equalization is determined. The multi-level linear equation system is solved to obtain the equalization result. This embodiment can significantly reduce the computational complexity of the equalization process in high-dynamic scenarios to resist inter-symbol interference and inter-carrier interference in multi-carrier modulation. It maintains excellent performance even under extreme conditions with zero cyclic prefix length, demonstrating strong scenario adaptability and system practicality.

[0071] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 Step S20 may include steps S201 to S205: Step S201: Based on the sparse structure of the inverse core matrix, determine the structure of the real diagonal matrix and the structure of the lower triangular matrix; Understandably, this is because finding the inverse core matrix... The structure of the lower triangular matrix is ​​known, therefore... and real diagonal matrix The structure is known; in the specific implementation, only the lower triangular matrix needs to be calculated. and real diagonal matrix The non-zero elements.

[0072] Step S202: Calculate the non-zero elements of the real diagonal matrix column by column, and calculate the non-zero elements of the lower triangular matrix column by column; It should be noted that the non-zero elements of a real diagonal matrix can be calculated according to the following formula:

[0073] In the formula, For the first The non-zero elements of the real diagonal matrix of the column, This is the column index value of the real diagonal matrix, that is, the value taken from the column index.

[0074] In one feasible implementation, calculating the non-zero elements of the lower triangular matrix column by column may include steps S2021~S2023: Step S2021: Determine the range of column index values ​​based on the current symbol index value, the number of subcarriers, and the number of symbols; It should be noted that the current symbol index value is the index value of the symbol block corresponding to the lower triangular matrix currently being calculated, and is usually represented as... The number of subcarriers, or the total number of subcarriers, is usually expressed as... The corresponding lower triangular matrix at this time Together with The number of symbols, or the number of symbol blocks, is usually expressed as... .

[0075] It is understandable that the column index value range, i.e., the range of subscript values ​​in the column containing the non-zero elements of the lower triangular matrix, is determined based on the current symbol index value, the number of subcarriers, and the number of symbols. The lower limit of the column index value range is... The upper limit of the column index value range is .

[0076] Step S2022: When the current column index value matches the range of the column index value, determine the range of the row index value corresponding to the current column index value based on the matching value range of the current column index value; It should be noted that the current column index value is the subscript value of the column containing the non-zero element of the lower triangular matrix currently being calculated, and the row index value range is the subscript value range of the row containing the non-zero element of the lower triangular matrix. Generally speaking, if the current column index value falls into different value ranges, the corresponding row index value range will be different.

[0077] Additionally, it should be noted that the matching value range refers to the value range into which the current column index value falls.

[0078] It is understandable that if the current column index value exist If the range is met, then begin calculating the index value of the current column. Non-zero elements in the corresponding column In addition, it is necessary to determine whether it has been completed. time If the calculation is complete, it indicates the current symbol index value. Once all non-zero elements of the corresponding lower triangular matrix have been calculated, the current sign index value will be... Updated to Let's begin the next round. Calculation. If not completed, the process will proceed normally, starting with the step of determining the range of row index values ​​corresponding to the current column index value by matching the range of values ​​for the current column index value.

[0079] In one feasible implementation, step S2022 may include steps A11 to A15: Step A11: When the matching value range of the current column index value is the value range of the first column, determine the lower limit of the first row index value based on the current column index value and a preset constant, and determine the upper limit of the first row index value based on the current column index value, the number of channel delay paths, and the preset constant. It should be noted that the lower limit of the value range for the first column is determined based on the lower limit of the column index value range, that is... The upper limit of the value range in the first column is determined by the current symbol index value. , the number of subcarriers , length of cyclic prefix Number of channel delay paths Determined, that is At this point, the value range of the first column is... .

[0080] Understandably, the default constant is usually 1, based on the current column index value. The lower limit of the index value of the first row is determined by a preset constant, that is... Based on the current column index value Number of channel delay paths And a preset constant determines the upper limit of the index value of the first row, i.e. .

[0081] Step A12: Determine the range of the first row index value based on the lower limit and the upper limit of the first row index value; It should be noted that the lower limit of the first row index value is the lower limit of the range of the first row index values, and the upper limit of the first row index value is the upper limit of the range of the first row index values.

[0082] It is understandable that the range of the first row's index value is determined based on the lower limit and the upper limit of the first row's index value. .

[0083] Step A13: Determine the lower limit and upper limit of the second row index value based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant; Understandably, this is based on the upper limit of the column index value range. , length of cyclic prefix And a preset constant determines the lower limit of the index value of the second row, i.e. The upper limit of the index value for the second row is determined based on the upper limit of the column index value range. .

[0084] A14. Determine the range of the second row index value based on the lower limit and the upper limit of the second row index value; It should be noted that the lower limit of the index value in the second row is the lower limit of the range of index values ​​in the second row, and the upper limit of the index value in the second row is the upper limit of the range of index values ​​in the second row.

[0085] It is understandable that the range of the second row's index value is determined based on the lower limit and the upper limit of the second row's index value. .

[0086] A15, take the first row index value range and the second row index value range as the row index value range corresponding to the current column index value.

[0087] It is understandable that if the matching value range of the current column index value is the value range of the first column, the range of the row index value corresponding to the current column index value includes two ranges, namely the range of the first row index value. range of index values ​​in the second row .

[0088] In another feasible implementation, step S2022 may include steps B11-B13: Step B11: When the matching value range of the current column index value is the value range of the second column, determine the lower limit of the third row index value based on the current column index value and a preset constant, and determine the upper limit of the third row index value based on the upper limit of the column index value range; It should be noted that the lower limit of the value range in the second column is based on the current symbol index value. , the number of subcarriers , length of cyclic prefix Number of channel delay paths And the preset constant is determined, that is The upper limit of the value range in the second column is determined by the current symbol index value. , the number of subcarriers , length of cyclic prefix Number of channel delay paths And the preset constant is determined, that is At this point, the value range of the second column is... .

[0089] Understandably, based on the current column index value The lower limit of the index value of the third row is determined by a preset constant, i.e. The upper limit of the index value for the third row is determined based on the upper limit of the column index value range. .

[0090] Step B12: Determine the range of the third row index value based on the lower limit and the upper limit of the third row index value; It should be noted that the lower limit of the index value in the third row is the lower limit of the range of index values ​​in the third row, and the upper limit of the index value in the third row is the upper limit of the range of index values ​​in the third row.

[0091] It is understandable that the range of the third row index value is determined based on the lower limit and the upper limit of the third row index value. .

[0092] Step B13: Use the range of index values ​​in the third row as the range of row index values ​​corresponding to the current column index value.

[0093] It is understandable that if the matching value range of the current column index value is the value range of the second column, the range of the row index value corresponding to the current column index value includes a range, namely the range of the third row index value. .

[0094] In one feasible implementation, step S2022 may include steps C11 to C15: C11, when the matching value range of the current column index value is the value range of the third column, determine the lower limit of the fourth row index value based on the upper limit of the column index value range and a preset constant, and determine the upper limit of the fourth row index value based on the current column index value, the cyclic prefix length, the number of channel delay paths and the preset constant; It should be noted that the lower limit of the value range in the third column is based on the current symbol index value. , the number of subcarriers , length of cyclic prefix Number of channel delay paths And the preset constant is determined, that is The upper limit of the value range in the third column is determined by the upper limit of the column index value range, that is... At this point, the value range of the third column is... .

[0095] It is understandable that the lower limit of the fourth row index value is determined based on the upper limit of the column index value range and a preset constant. Based on the current column index value , length of cyclic prefix Number of channel delay paths And a preset constant determines the upper limit of the index value of the fourth row, i.e. .

[0096] C12, determine the range of the fourth row index value based on the lower limit and the upper limit of the fourth row index value; It should be noted that the lower limit of the index value in the fourth row is the lower limit of the range of index values ​​in the fourth row, and the upper limit of the index value in the fourth row is the upper limit of the range of index values ​​in the fourth row.

[0097] It is understandable that the range of the fourth row's index value is determined based on the lower limit and the upper limit of the fourth row's index value. .

[0098] C13, determine the lower limit of the index value of the fifth row based on the upper limit of the column index value range, the length of the loop prefix and the preset constant, and determine the upper limit of the index value of the fifth row based on the upper limit of the column index value range; Understandably, this is based on the upper limit of the column index value range and the length of the loop prefix. And a preset constant determines the lower limit of the index value of the fifth row, i.e. The upper limit of the index value for the fifth row is determined based on the upper limit of the column index value range. .

[0099] C14. Determine the range of the fifth row index value based on the lower limit and the upper limit of the fifth row index value; It should be noted that the lower limit of the index value in the fifth row is the lower limit of the range of index values ​​in the fifth row, and the upper limit of the index value in the fifth row is the upper limit of the range of index values ​​in the fifth row.

[0100] It is understandable that the range of the fifth row index value is determined based on the lower limit and the upper limit of the fifth row index value. .

[0101] C15, take the index value range of the third row, the index value range of the fourth row, and the index value range of the fifth row as the row index value range corresponding to the current column index value.

[0102] It is understandable that if the matching value range of the current column index value is the value range of the third column, the range of the row index value corresponding to the current column index value includes three ranges, that is, the range of the third row index value. The range of index values ​​in the fourth row range of index values ​​in the fifth row .

[0103] Step S2023: Based on the time-domain channel matrix and the real diagonal matrix, calculate the non-zero elements of the lower triangular matrix within the range of the row index values ​​corresponding to the current column index value.

[0104] If the current row index value Matches the range of the first row's index value Then in hour, ;exist hour, ;exist hour, .

[0105] If the current row index value Matches the range of index values ​​in the second row ,but .

[0106] If the current row index value Matches the range of the index value in the third row Then in hour, ;exist hour, .

[0107] If the current row index value Matches the range of index values ​​in the fourth row ,but .

[0108] If the current row index value Matches the range of index values ​​in the fifth row ,but .

[0109] Step S203: Determine the real diagonal matrix based on the non-zero elements of the real diagonal matrix and the structure of the real diagonal matrix; It should be noted that the real diagonal matrix is ​​obtained by considering the non-zero elements and structure of the real diagonal matrix. .

[0110] Step S204: Determine the lower triangular matrix based on the non-zero elements of the lower triangular matrix and the structure of the lower triangular matrix; It is understandable that the lower triangular matrix is ​​obtained by considering its non-zero elements and structure. .

[0111] Step S205: Based on the lower triangular matrix, determine the conjugate transpose of the lower triangular matrix.

[0112] It is understandable that, based on the obtained lower triangular matrix, the conjugate transpose is performed to determine the conjugate transpose matrix of the lower triangular matrix. .

[0113] This embodiment provides an interference equalization method for multi-carrier modulation in high-dynamic scenarios. It calculates the non-zero elements of the real diagonal matrix column by column, and then calculates the non-zero elements of the lower triangular matrix column by column. Based on the non-zero elements and structure of the real diagonal matrix, the real diagonal matrix is ​​determined; based on the non-zero elements and structure of the lower triangular matrix, the lower triangular matrix is ​​determined; and based on the real diagonal matrix, the conjugate transpose of the real diagonal matrix is ​​determined. This embodiment can significantly reduce the computational complexity of the equalization process in resisting inter-symbol interference and inter-carrier interference in multi-carrier modulation in high-dynamic scenarios. It maintains excellent performance even under extreme conditions where the cyclic prefix length is zero, demonstrating strong scenario adaptability and system practicality.

[0114] For example, to help understand the implementation process of the interference equalization method for multi-carrier modulation in high dynamic scenarios obtained by combining this embodiment with the above-described embodiment two, please refer to... Figure 6 , Figure 6 A simplified flowchart of an interference equalization method for multi-carrier modulation in high dynamic scenarios is provided, specifically: First, obtain the time-domain channel matrix of the time-frequency bidispersive channel. ; Then based on the time-domain channel matrix The structure of the MMSE equilibrium matrix that needs to be inverted is obtained from the structure of the MMSE equilibrium matrix. ; For matrix conduct Decomposition. Where due to the matrix The structure of the lower triangular matrix is ​​known, therefore... and real diagonal matrix The structure is known; in practice, only the lower triangular matrix needs to be calculated. and real diagonal matrix The non-zero elements.

[0115] The MMSE equilibrium process is transformed into a system of multilevel linear equations to be solved:

[0116] Using matrices Given the sparsity of the vectors, calculate the vector on the right: .

[0117] Using matrices Due to the sparsity of the equations, we solve them forward: , to obtain vector .

[0118] Using matrices Due to sparsity, diagonal scaling is applied: , to obtain vector .

[0119] Using matrices Due to the sparsity of the equations, the backward solution method is used: The vector after MMSE equilibrium is obtained. .

[0120] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the interference equalization method of multi-carrier modulation in high dynamic scenarios of this application. Any simple transformations based on this technical concept are all within the protection scope of this application.

[0121] This application also provides an interference equalization device for multi-carrier modulation in high dynamic scenarios. Please refer to [reference needed]. Figure 7 Interference equalization devices for multi-carrier modulation in high dynamic scenarios include: The inverse characterization module 10 is used to obtain the time-domain channel matrix of the time-frequency dual-dispersion channel and, based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix. The matrix decomposition module 20 is used to decompose the inverse core matrix based on the sparse structure of the inverse core matrix to obtain the decomposed matrix. The equalization transformation module 30 is used to determine the multi-level linear equation system with minimum mean square error equalization based on the time domain channel matrix, the inverse core matrix and the decomposition matrix. The equation solving module 40 is used to solve the multi-level linear equation system to obtain the equilibrium result.

[0122] In one feasible implementation, the decomposition matrix includes a lower triangular matrix, a real diagonal matrix, and the conjugate transpose of the lower triangular matrix. The matrix decomposition module 20 is further used to determine the structure of the real diagonal matrix and the structure of the lower triangular matrix based on the sparsity structure of the inverse core matrix. Calculate the non-zero elements of the real diagonal matrix column by column, and calculate the non-zero elements of the lower triangular matrix column by column; The real diagonal matrix is ​​determined based on the non-zero elements of the real diagonal matrix and the structure of the real diagonal matrix; The lower triangular matrix is ​​determined based on the non-zero elements of the lower triangular matrix and the structure of the lower triangular matrix; Based on the lower triangular matrix, determine the conjugate transpose of the lower triangular matrix.

[0123] In one feasible implementation, the matrix decomposition module 20 is further configured to determine the range of column index values ​​based on the current symbol index value, the number of subcarriers, and the number of symbols; When the current column index value matches the range of the column index value, the range of the row index value corresponding to the current column index value is determined based on the matching value range of the current column index value; Based on the time-domain channel matrix and the real diagonal matrix, calculate the non-zero elements of the lower triangular matrix within the range of the row index values ​​corresponding to the current column index value.

[0124] In one feasible implementation, the matrix factorization module 20 is further configured to determine the lower limit of the first row index value based on the current column index value and a preset constant when the matching value range of the current column index value is the value range of the first column, and to determine the upper limit of the first row index value based on the current column index value, the number of channel delay paths and the preset constant; The range of the first row index value is determined based on the lower limit and the upper limit of the first row index value; The lower limit of the second row index value is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the second row index value is determined based on the upper limit of the column index value range. The range of the second row index value is determined based on the lower limit and the upper limit of the second row index value; The range of index values ​​in the first row and the range of index values ​​in the second row are used as the range of row index values ​​corresponding to the current column index value.

[0125] In one feasible implementation, the matrix factorization module 20 is further configured to determine the lower limit of the third row index value based on the current column index value and a preset constant when the matching value range of the current column index value is the value range of the second column, and to determine the upper limit of the third row index value based on the upper limit of the column index value range; The range of the third row index value is determined based on the lower limit and the upper limit of the third row index value. Use the range of index values ​​in the third row as the range of row index values ​​corresponding to the current column index value.

[0126] In one feasible implementation, the matrix factorization module 20 is further configured to determine the lower limit of the fourth row index value based on the upper limit of the column index value range and a preset constant when the matching value range of the current column index value is the value range of the third column, and to determine the upper limit of the fourth row index value based on the current column index value, the cyclic prefix length, the number of channel delay paths and the preset constant. The range of the fourth row index value is determined based on the lower limit and the upper limit of the fourth row index value. The lower limit of the index value of the fifth row is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the index value of the fifth row is determined based on the upper limit of the column index value range. The range of the fifth row index value is determined based on the lower limit and the upper limit of the fifth row index value. The range of index values ​​in the third row, the range of index values ​​in the fourth row, and the range of index values ​​in the fifth row are used as the range of row index values ​​corresponding to the current column index value.

[0127] In one feasible implementation, the equation solving module 40 is further used to calculate the product of the conjugate transpose of the time-domain channel matrix and the received signal vector based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, to obtain the right-hand vector. Based on the sparse structure of the lower triangular matrix, the first equation between the lower triangular matrix, the first intermediate result vector, and the right-hand vector is solved forward to obtain the first intermediate result vector. Based on the sparse structure of the real diagonal matrix, the first intermediate result vector is diagonally scaled to obtain the second intermediate result vector; Based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, the second equation between the conjugate transpose of the lower triangular matrix, the equalization result, and the second intermediate result vector is solved backward to obtain the equalization result.

[0128] The interference equalization device for multi-carrier modulation in high-dynamic scenarios provided in this application adopts the interference equalization method for multi-carrier modulation in high-dynamic scenarios described in the above embodiments, which can solve the technical problem of high complexity in equalization schemes for resisting inter-symbol interference and inter-carrier interference in high-dynamic scenarios. Compared with the prior art, the beneficial effects of the interference equalization device for multi-carrier modulation in high-dynamic scenarios provided in this application are the same as those of the interference equalization method for multi-carrier modulation in high-dynamic scenarios provided in the above embodiments, and other technical features in the interference equalization device for multi-carrier modulation in high-dynamic scenarios are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0129] This application provides an interference equalization device for multi-carrier modulation in high dynamic scenarios. The interference equalization device for multi-carrier modulation in high dynamic scenarios includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the interference equalization method for multi-carrier modulation in high dynamic scenarios in the above embodiment 1.

[0130] The following is for reference. Figure 8This document illustrates a structural schematic diagram of an interference equalization device suitable for implementing multi-carrier modulation in high-dynamic scenarios according to embodiments of this application. The interference equalization device for multi-carrier modulation in high-dynamic scenarios in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 8 The interference equalization device for multi-carrier modulation in high dynamic scenarios shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0131] like Figure 8 As shown, the interference equalization device for multi-carrier modulation in high dynamic scenarios may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the interference equalization device for multi-carrier modulation in high dynamic scenarios. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the multi-carrier modulation interference equalization device in high-dynamic scenarios to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a multi-carrier modulation interference equalization device in a high-dynamic scenario with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0132] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0133] The interference equalization device for multi-carrier modulation in high-dynamic scenarios provided in this application adopts the interference equalization method for multi-carrier modulation in high-dynamic scenarios described in the above embodiments, which can solve the technical problem of high complexity in equalization schemes for resisting inter-symbol interference and inter-carrier interference in high-dynamic scenarios. Compared with the prior art, the beneficial effects of the interference equalization device for multi-carrier modulation in high-dynamic scenarios provided in this application are the same as those of the interference equalization method for multi-carrier modulation in high-dynamic scenarios provided in the above embodiments, and other technical features in this interference equalization device for multi-carrier modulation in high-dynamic scenarios are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0134] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0135] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0136] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the interference equalization method for multi-carrier modulation in high dynamic scenarios described in the above embodiments.

[0137] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0138] The aforementioned computer-readable storage medium may be included in a multi-carrier modulation interference equalization device for high dynamic scenarios; or it may exist independently and not be assembled into a multi-carrier modulation interference equalization device for high dynamic scenarios.

[0139] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a multi-carrier modulation interference equalization device in a high-dynamic scenario, the multi-carrier modulation interference equalization device in the high-dynamic scenario performs the following: acquires the time-domain channel matrix of the time-frequency bidispersive channel; determines the inverse core matrix in the minimum mean square error equalization matrix based on the time-domain channel matrix; decomposes the inverse core matrix based on its sparsity structure to obtain a decomposed matrix; determines a multi-level linear equation system for minimum mean square error equalization based on the time-domain channel matrix, the inverse core matrix, and the decomposed matrix; and solves the multi-level linear equation system to obtain the equalization result.

[0140] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0142] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0143] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the interference equalization method for multi-carrier modulation in high-dynamic scenarios described above. This solves the technical problem of high complexity in equalization schemes to resist inter-symbol interference and inter-carrier interference in high-dynamic scenarios. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the interference equalization method for multi-carrier modulation in high-dynamic scenarios provided in the above embodiments, and will not be repeated here.

[0144] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described above.

[0145] The computer program product provided in this application can solve the technical problem of high complexity in equalization schemes for resisting inter-symbol interference and inter-carrier interference in multi-carrier modulation under high dynamic scenarios. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the interference equalization method for multi-carrier modulation under high dynamic scenarios provided in the above embodiments, and will not be repeated here.

[0146] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An interference equalization method for multi-carrier modulation in high dynamic scenarios, characterized in that, The method includes: Obtain the time-domain channel matrix of the time-frequency dual-dispersion channel, and based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix; Based on the sparse structure of the inverse core matrix, the inverse core matrix is ​​decomposed to obtain the decomposed matrix; Based on the time-domain channel matrix, the inverse core matrix, and the decomposition matrix, a multi-level linear equation system with minimum mean square error equilibrium is determined. The equilibrium result is obtained by solving the system of multilevel linear equations.

2. The method as described in claim 1, characterized in that, The decomposition matrix includes a lower triangular matrix, a real diagonal matrix, and the conjugate transpose of the lower triangular matrix; The step of decomposing the inverted core matrix based on its sparse structure to obtain the decomposed matrix includes: Based on the sparse structure of the inverse core matrix, the structure of the real diagonal matrix and the structure of the lower triangular matrix are determined. Calculate the non-zero elements of the real diagonal matrix column by column, and calculate the non-zero elements of the lower triangular matrix column by column; The real diagonal matrix is ​​determined based on the non-zero elements of the real diagonal matrix and the structure of the real diagonal matrix; The lower triangular matrix is ​​determined based on the non-zero elements of the lower triangular matrix and the structure of the lower triangular matrix; Based on the lower triangular matrix, determine the conjugate transpose of the lower triangular matrix.

3. The method as described in claim 2, characterized in that, The steps for calculating the non-zero elements of a lower triangular matrix column by column include: The range of column index values ​​is determined based on the current symbol index value, the number of subcarriers, and the number of symbols. When the current column index value matches the range of the column index value, the range of the row index value corresponding to the current column index value is determined based on the matching value range of the current column index value; Based on the time-domain channel matrix and the real diagonal matrix, calculate the non-zero elements of the lower triangular matrix within the range of the row index values ​​corresponding to the current column index value.

4. The method as described in claim 3, characterized in that, The steps for determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value include: When the matching value range of the current column index value is the value range of the first column, the lower limit of the first row index value is determined based on the current column index value and a preset constant, and the upper limit of the first row index value is determined based on the current column index value, the number of channel delay paths, and the preset constant. The range of the first row index value is determined based on the lower limit and the upper limit of the first row index value; The lower limit of the second row index value is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the second row index value is determined based on the upper limit of the column index value range. The range of the second row index value is determined based on the lower limit and the upper limit of the second row index value; The range of index values ​​in the first row and the range of index values ​​in the second row are used as the range of row index values ​​corresponding to the current column index value.

5. The method as described in claim 3, characterized in that, The steps for determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value include: When the matching value range of the current column index value is the value range of the second column, the lower limit of the third row index value is determined based on the current column index value and a preset constant, and the upper limit of the third row index value is determined based on the upper limit of the column index value range; The range of the third row index value is determined based on the lower limit and the upper limit of the third row index value. Use the range of index values ​​in the third row as the range of row index values ​​corresponding to the current column index value.

6. The method as described in claim 5, characterized in that, The steps for determining the range of row index values ​​corresponding to the current column index value based on the matching value range of the current column index value include: When the matching value range of the current column index value is the value range of the third column, the lower limit of the fourth row index value is determined based on the upper limit of the column index value range and a preset constant, and the upper limit of the fourth row index value is determined based on the current column index value, the cyclic prefix length, the number of channel delay paths and the preset constant. The range of the fourth row index value is determined based on the lower limit and the upper limit of the fourth row index value. The lower limit of the index value of the fifth row is determined based on the upper limit of the column index value range, the length of the loop prefix, and the preset constant, and the upper limit of the index value of the fifth row is determined based on the upper limit of the column index value range. The range of the fifth row index value is determined based on the lower limit and the upper limit of the fifth row index value. The range of index values ​​in the third row, the range of index values ​​in the fourth row, and the range of index values ​​in the fifth row are used as the range of row index values ​​corresponding to the current column index value.

7. The method as described in claim 2, characterized in that, The steps for solving the multilevel linear equation system to obtain the equilibrium result include: Based on the sparse structure of the conjugate transpose of the time-domain channel matrix, the product of the conjugate transpose of the time-domain channel matrix and the received signal vector is calculated to obtain the right-hand vector. Based on the sparse structure of the lower triangular matrix, the first equation between the lower triangular matrix, the first intermediate result vector, and the right-hand vector is solved forward to obtain the first intermediate result vector. Based on the sparse structure of the real diagonal matrix, the first intermediate result vector is diagonally scaled to obtain the second intermediate result vector; Based on the sparsity structure of the conjugate transpose of the time-domain channel matrix, the second equation between the conjugate transpose of the lower triangular matrix, the equalization result, and the second intermediate result vector is solved backward to obtain the equalization result.

8. An interference equalization device for multi-carrier modulation in high dynamic scenarios, characterized in that, The device includes: The inverse characterization module is used to obtain the time-domain channel matrix of the time-frequency dual-dispersion channel, and based on the time-domain channel matrix, determine the inverse core matrix in the minimum mean square error equalization matrix. The matrix decomposition module is used to decompose the inverse core matrix based on the sparse structure of the inverse core matrix to obtain the decomposed matrix; The equalization transformation module is used to determine the multi-level linear equation system with minimum mean square error equalization based on the time domain channel matrix, the inverse core matrix, and the decomposition matrix. The equation solving module is used to solve the multi-level linear equation system and obtain the equilibrium result.

9. An interference equalization device for multi-carrier modulation in high dynamic scenarios, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the interference equalization method for multi-carrier modulation in high dynamic scenarios as described in any one of claims 1 to 7.

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