Channel estimation method, apparatus, device, and storage medium

Abstract

The application discloses a channel estimation method, device, equipment and storage medium, when estimating the channel of a large-scale MIMO-OFDM system by using an ISD algorithm, a block sparse equivalent support set of a channel impulse response of a base station is constructed according to a sending signal of the base station; wherein the block sparse equivalent support set contains a plurality of element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed by a preset value of the channel impulse response; in the support set reconstruction process, the element blocks in the block sparse equivalent support set are updated, the mechanism considering the block sparsity can increase the robustness of support set detection, thereby improving the channel estimation performance, when the length of the detected block sparse equivalent support set satisfies a preset iteration stop condition, the updating of the block sparse equivalent support set is stopped, and the block sparse equivalent support set in the last iteration is output as an estimated value of the channel impulse response, the block sparsity of the large-scale MIMO-OFDM system can be explored, the channel estimation performance is improved, and the output CIR is accurate.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a channel estimation method, apparatus, device, and storage medium. Background Technology

[0002] Channel estimation is the process of estimating the model parameters of a hypothetical channel model from received data. Through channel estimation, the receiver can obtain the channel's CIR (Channel Impulse Response), thus providing the necessary CSI (Channel State Information) for subsequent coherent demodulation. In large-scale MIMO-OFDM systems, the energy of the wireless channel is almost entirely concentrated in a few components of the CIR, making the channel naturally block-sparse. In sparse channel estimation algorithms, convex optimization algorithms use mathematical methods to transform non-convex problems into convex problems for solution, thereby achieving sparse signal reconstruction. The ISD (Iterative Support Detection) algorithm is one such improved convex optimization algorithm, and therefore, it is commonly used in channel estimation for large-scale MIMO-OFDM systems.

[0003] However, the ISD algorithm is an improved convex optimization algorithm. It was proposed to address the problem of suboptimal signal reconstruction caused by the high computational complexity of the BP (Basis Pursuit) algorithm in solving for the minimum norm. Its core idea is to improve the reconstruction performance of the BP algorithm through iterative support set detection, while also reducing the computational complexity of the BP algorithm. Considering the spatiotemporal correlation in the multipath propagation process of practical large-scale MIMO-OFDM systems, the CIR of different transmit and receive antenna pairs exhibits block sparsity. However, the existing ISD algorithm does not explore the block sparsity of large-scale MIMO-OFDM systems, resulting in inaccurate channel estimation and incorrect output CIR. Summary of the Invention

[0004] The purpose of this invention is to provide a channel estimation method, apparatus, device, and storage medium that can explore the block sparsity of large-scale MIMO-OFDM systems, improve channel estimation performance, and output accurate CIR.

[0005] To achieve the above objectives, embodiments of the present invention provide a channel estimation method, comprising:

[0006] A block sparse equivalent support set for the channel impulse response of the base station is constructed based on the transmitted signal of the base station; wherein, the block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response;

[0007] Update the element blocks in the block sparse equivalent support set;

[0008] When the length of the block sparse equivalent support set is detected to meet the preset iteration stop condition, the update of the block sparse equivalent support set is stopped.

[0009] The output of the block sparse equivalent support set of the last iteration is an estimate of the channel impulse response.

[0010] As an improvement to the above scheme, the step of constructing the block sparse equivalent support set of the base station's channel impulse response based on the base station's transmitted signal includes:

[0011] Calculate the pilot signal received by the user terminal based on the base station's transmitted signal;

[0012] The conventional support set of the channel impulse response is constructed based on the pilot signal received by the user terminal; wherein the conventional support set contains several element blocks, and each element block contains several elements;

[0013] The conventional support set is equivalent to the number of element blocks and the number of elements to obtain the block sparse equivalent support set of the channel impulse response.

[0014] As an improvement to the above scheme, the step of calculating the received pilot signal at the user end based on the base station's transmitted signal includes:

[0015] Acquire the pilot signals sent by the base station;

[0016] The pilot signal received by the user terminal is calculated based on the pilot signal sent by the base station and the preset value of the channel impulse response.

[0017] As an improvement to the above scheme, updating the element blocks in the block sparse equivalent support set includes:

[0018] Obtain the ordered signal of the channel impulse response, and construct the ordered signal support set of the ordered signal;

[0019] The target ordered signal is found from the ordered signals based on the preset minimum signal difference.

[0020] The ordered signal support set is updated using the target ordered signal as a threshold.

[0021] Calculate the intersection of the ordered signal support set and the block sparse equivalent support set;

[0022] The intersection is updated based on the index number of the element blocks to update the block sparse equivalent support set.

[0023] As an improvement to the above scheme, obtaining the ordered signal of the channel impulse response includes:

[0024] The channel impulse responses are sorted in ascending order to obtain ordered signals of the channel impulse responses.

[0025] As an improvement to the above scheme, the iteration stopping condition is: the length of the block sparse equivalent support set is less than a preset pilot number threshold.

[0026] As an improvement to the above scheme, the pilot number threshold is the difference between the total number of subcarriers of the base station and the number of pilots; wherein, the total number of subcarriers is calculated by multiplying the total number of antennas of the base station and the number of subcarriers per antenna.

[0027] To achieve the above objectives, embodiments of the present invention also provide a channel estimation apparatus, comprising:

[0028] A support set construction module is used to construct a block sparse equivalent support set of the channel impulse response of the base station based on the transmitted signal of the base station; wherein, the block sparse equivalent support set contains a number of element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response;

[0029] The support set update module is used to update the element blocks in the block sparse equivalent support set, and to stop updating the block sparse equivalent support set when the length of the block sparse equivalent support set is detected to meet a preset iteration stop condition.

[0030] The channel impulse response estimation output module is used to output the block sparse equivalent support set of the last iteration as the estimated value of the channel impulse response.

[0031] To achieve the above objectives, embodiments of the present invention also provide a channel estimation device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the channel estimation method as described in any of the above embodiments.

[0032] To achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the channel estimation method as described in any of the above embodiments.

[0033] Compared to existing technologies, the channel estimation method, apparatus, device, and storage medium disclosed in this invention, when estimating the channel of a large-scale MIMO-OFDM system using the ISD algorithm, constructs a block sparse equivalent support set of the base station's channel impulse response based on the base station's transmitted signals. The block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and is constructed from preset values ​​of the channel impulse response. During support set reconstruction, the element blocks in the block sparse equivalent support set are updated. This mechanism, which considers block sparsity, increases the robustness of support set detection, thereby improving channel estimation performance. When the length of the block sparse equivalent support set reaches a preset iteration stopping condition, updating the block sparse equivalent support set stops, and the last iteration's block sparse equivalent support set is output as an estimate of the channel impulse response. This approach can explore the block sparsity of large-scale MIMO-OFDM systems, improve channel estimation performance, and output accurate CIR. Attached Figure Description

[0034] Figure 1 This is a flowchart of a channel estimation method provided in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram illustrating the generation of block sparse equivalent CIR provided in an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram comparing the NMSE performance of the BP algorithm, ISD algorithm and IISD algorithm provided in the embodiments of the present invention;

[0037] Figure 4 This is a structural block diagram of a channel estimation device provided in an embodiment of the present invention;

[0038] Figure 5 This is a structural block diagram of a channel estimation device provided in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] See Figure 1 , Figure 1 This is a flowchart of a channel estimation method provided in an embodiment of the present invention. The channel estimation method includes:

[0041] S1. Construct a block sparse equivalent support set for the channel impulse response of the base station based on the transmitted signal of the base station;

[0042] S2. Update the element blocks in the block sparse equivalent support set;

[0043] S3. When it is detected that the length of the block sparse equivalent support set has reached the preset iteration stop condition, the update of the block sparse equivalent support set is stopped.

[0044] S4. Output the block sparse equivalent support set of the last iteration as an estimate of the channel impulse response.

[0045] Specifically, in step S1, the block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response. Constructing the block sparse equivalent support set of the base station's channel impulse response based on the base station's transmitted signal includes:

[0046] S11. Calculate the pilot signal received by the user terminal based on the base station's transmitted signal;

[0047] S12. Construct a conventional support set for the channel impulse response based on the pilot signal received by the user terminal; wherein, the conventional support set contains several element blocks, and each element block contains several elements;

[0048] S13. The conventional support set is equivalent to the number of element blocks and the number of elements to obtain the block sparse equivalent support set of the channel impulse response.

[0049] Specifically, in step S11, the step of calculating the received pilot signal of the user terminal based on the transmitted signal of the base station includes: acquiring the pilot signal transmitted by the base station; and calculating the received pilot signal of the user terminal based on the pilot signal transmitted by the base station and a preset value of the channel impulse response.

[0050] For example, consider a large-scale MIMO-OFDM system with M antennas at the base station and U users per antenna (M >> U). Assume the preset channel impulse response between the m-th transmitting antenna in the OFDM (Orthogonal Frequency Division Multiplexing) symbol and a user within the cell is expressed as:

[0051] h m =[h m (0),h m (1),...,h m (L-1)] T (1);

[0052] Where L is the maximum channel delay spread, and is the difference between the maximum transmission delay and the minimum transmission delay.

[0053] For a real transmission channel, vector h m =[h m (0),h m (1),...,h m (L-1)] T The vector h contains only a small number of non-zero values, and we let its number be K. Therefore, it's easy to see that the number of non-zero values ​​K in vector h is much smaller than L, making vector h sparse. Consider a system with N subcarriers for OFDM symbols, and X pilot signals (pilot and data) transmitted by the m-th transmitting antenna of the base station. m Then the pilot signal y received by the user terminal can be expressed as:

[0054]

[0055] Where F represents an N×N DFT matrix, F L represents the partial matrix taken from the first L columns of matrix F, and n represents the additive white Gaussian noise vector (AWGN).

[0056] After DFT (Discrete Fourier Transform Formula), the pilot signal y in OFDM Ω It can be represented as:

[0057]

[0058] Among them, y Ω It is obtained from the pilot signal y received from the user end, and Ω is the index set of subcarriers assigned to the pilot, which can be randomly selected from the subcarrier set [1,2,3,...,N]. P m =diag{p m} indicates that p m It is a diagonal matrix with diagonal lines. Let N be the pilot vector of the m-th transmitting antenna. P F represents the number of pilots in an OFDM symbol. L|Ω It is a partial matrix F selected based on the index set Ω. L A submatrix consisting of certain rows, n Ω It is represented as an additive complex Gaussian white noise vector.

[0059] Equation (3) above can be further simplified, and its expression can be expressed as:

[0060] y Ω =Θh+n Ω (4);

[0061] Where Θ=[P1F L|Ω P2FL|Ω ,...,P M F L|Ω ] is an N P A matrix of size ×ML It is a CIR of size ML×1.

[0062] Specifically, in steps S12 to S13, the spatiotemporal common sparsity in a large-scale MIMO-OFDM system can improve the reconstruction accuracy of sparse signals. Considering R consecutive OFDM symbols, we can conclude that:

[0063] Y Ω =ΘH+N Ω (5);

[0064] Among them, Y Ω =[y k ,y k+1 ,...,y k+R-1 ], H = [h k ,h k+1 ,...,h k+R-1 ], N Ω =[n k ,n k+1 ,...,n k+R-1 ].

[0065] For example, to reconstruct a large-scale MIMO-OFDM system Based on the shared spatial sparsity between base station antennas and user-end single antennas, it can be concluded that base station antennas and user-end single antennas exhibit very similar path delays. They share a common support set. If we use... h i From the regular support set, we can derive: in, This special structure, such as Figure 2 As shown, the gray blocks represent non-zero elements of the CIR, and the blank areas represent zero elements of the CIR.

[0066] Because CIRs share a common support set with different transmitting antennas, h can be used. i Elements are grouped, and elements with the same index are grouped into non-zero blocks and zero blocks. Figure 2 In this context, let the generated block sparse equivalent channel impulse response (CIR) be defined as g = [g1, g2, ..., g L If h and g are given, then the relationship between h and g can be expressed as:

[0067] g((l-1)M+m)=h((m-1)L+l) (6);

[0068] Where, l = [1,2,...,L], m = [1,2,...,M].

[0069] Depend on Figure 2 It can be seen that, based on the number of subcarriers in each antenna, the channel impulse response h is divided into L equal blocks, and each block has m elements. Therefore, in the l-th block g... l It consists of M consecutive elements in h that are all zero or non-zero. Therefore, by applying an equivalent value to the conventional support set based on the number of element blocks L and the number of elements m, the block sparse equivalent support set Γ of the channel impulse response can be obtained. g The block-sparse equivalent channel impulse response g also exhibits block sparsity, which indicates that we can convert the block-sparse equivalent support set Γ of g into a block-sparse equivalent channel response. g The M consecutive elements in Θ are treated as a whole. Therefore, based on equation (5), the columns of Θ are rearranged to obtain a new matrix P:

[0070] P(:,(l-1)M+m)=Θ(:,(m-1)L+l) (7);

[0071] From equation (7), equation (4) can be rewritten as:

[0072] y Ω =Pg+n Ω (8);

[0073] For g of size ML×1 and N of size P ×1 y Ω Due to the large number of antennas and limited pilot overhead, N P It is usually much smaller than ML, so this is also an underdetermined problem, which can be solved by the convex optimization method in the ISD algorithm.

[0074] Specifically, in steps S2 to S4, considering the spatiotemporal correlation of multipath propagation in actual large-scale MIMO-OFDM systems, the CIR of different transmit and receive antenna pairs exhibits block sparsity, while the ISD algorithm does not explore the block sparsity of large-scale MIMO-OFDM systems. Therefore, this embodiment of the invention proposes an improved iterative support detection algorithm (i.e., the IISD algorithm) based on the block sparsity in large-scale MIMO-OFDM systems.

[0075] It is worth noting that, compared to the ISD algorithm, the key difference in the IISD algorithm lies in its consideration of the block sparsity of large-scale MIMO-OFDM systems. For a non-zero element block in a block-sparse equivalent channel impulse response, assuming that consecutive elements of the block are non-zero, their indices should be included in the block-sparse equivalent support set. However, due to noise, some indices of the block may be incorrectly detected. Therefore, the element blocks in the block-sparse equivalent support set are updated by determining whether the element block is a non-zero block or a zero block. This mechanism, which considers block sparsity, increases the robustness of support detection, thereby improving channel estimation performance. Furthermore, compared to the ISD algorithm, the IISD algorithm only adds some comparison operations; therefore, the overall complexity of the IISD algorithm does not increase.

[0076] Specifically, step S2 includes:

[0077] S21. Obtain the ordered signal of the channel impulse response and construct the ordered signal support set of the ordered signal;

[0078] S22. Find the target ordered signal from the ordered signals according to the preset minimum signal difference;

[0079] S23. Update the ordered signal support set using the target ordered signal as a threshold;

[0080] S24. Calculate the intersection of the ordered signal support set and the block sparse equivalent support set;

[0081] S25. Update the intersection according to the index number of the element blocks to update the block sparse equivalent support set.

[0082] In this embodiment of the invention, the steps for updating the element blocks in the block sparse equivalent support set using the IISD algorithm (i.e., steps S21 to S25 above) are as follows:

[0083] Step 1: Iteration count s = 0, support set

[0084] Step 2: Calculate the complement: Among them, W (s) Indicates the current support set The complement;

[0085] Step 3: Reacquire the reconstruction support set:

[0086]

[0087] in, In the s-th iteration, the IISD algorithm uses the truncated basis pursuit (BP) method to solve the problem of minimizing the l1 norm of the truncation in step 3 to update the recovered signal g(s).

[0088] Step 4: Response to the channel impulse response g (s) The channel impulse response is sorted in ascending order to obtain the ordered signal v. (s) v (s) =Sort(g (s) );

[0089] Step 5: Calculate v (s) The difference between two adjacent signals, when satisfying the following equation (10), is used to find the target ordered signal v. (s) (i):

[0090] |v (s) (i+1)|-|v (s) (i)|>|τ s | (10);

[0091] Among them, |v (s) (i)| represents v (s) The absolute value of the i-th element. The smallest i is the ordered signal v. (s) The index of the "first significant jump" is defined as τ. (s) τ (s) =(LM) -1 ||v (s) || ∞ .

[0092] Step 6: Using the target ordered signal v (s) (i) is the threshold, and the threshold ε is defined. (s) ε (s) =|v (s) (i)|;

[0093] Step 7: Utilize the threshold ε (s) Update v (s) Ordered signal support set

[0094]

[0095] Step 8: Due to the block sparsity of g(s), the support set of g(s) It can be updated to:

[0096]

[0097] Equation (12) states that: by comparing the ordered signal support set and the block sparse equivalent support set, an intersection is obtained. For this intersection, the index number of the element blocks contained in the intersection (i.e., how many consecutive non-zero or zero elements are in an element block, and only consecutive non-zero or zero elements can be indexed) is two-thirds (2m / 3) of the length of the element block to determine whether the element block is a non-zero block or a zero block. Only when more than two-thirds of the index number of the element block is contained in the ordered signal support set will all M consecutive zero or non-zero elements of the element block be added to the block sparse equivalent support set, thereby completing one update of the block sparse equivalent support set.

[0098] Step 9: Increase the number of iterations: s = s + 1;

[0099] Step 10: When the length of the block sparse equivalent support set is detected to meet the preset iteration stop condition, execute step 11; otherwise, execute step S2 and continue to reconstruct the signal of the block sparse equivalent support set until the iteration stop condition is met.

[0100] Step 11: Output the block sparse equivalent support set of the last iteration as an estimate of the channel impulse response.

[0101] Specifically, the iteration stopping condition is: the length of the block sparse equivalent support set is less than a preset pilot number threshold; wherein, the pilot number threshold is the difference between the total number of subcarriers of the base station and the number of pilots, and the total number of subcarriers is calculated by multiplying the total number of antennas of the base station and the number of subcarriers per antenna.

[0102] For example, the length of the block sparse equivalent support set is less than a preset pilot number threshold, i.e., it satisfies... Represented as a block sparse equivalent support set The length of the antenna is M, the total number of antennas in the base station is L, the number of subcarriers per antenna is N. P The number of pilot frequencies.

[0103] Furthermore, the present invention provides experimental embodiments:

[0104] System simulation was performed using MATLAB software. Parameter settings included: 64 base station transmit antennas M, 4096 subcarriers per antenna L, 50MHz channel bandwidth, a maximum channel delay spread of 128, and all reconstruction algorithms using the same non-orthogonal pilot distribution with uniform pilot positions and a pilot number N. PThe value is 640, and the performance index used is mean square error (NMSE). NMSE is a commonly used measurement method to compare the deviation between measured and simulated values. It aims to measure the degree of deviation between the observed value and the theoretically calculated value on average. The larger the NMSE, the greater the measurement error, that is, the greater the deviation between the simulation and the measured (observation). Conversely, the smaller the NMSE, the closer the simulated value (measured value) is to the actual observed value, that is, the lower the deviation between the simulation and the measured (observation).

[0105] See Figure 3 , Figure 3 This diagram illustrates the NMSE performance comparison between the IISD algorithm provided in this invention and the classic ISD and BP algorithms. As can be seen from the diagram, the BP algorithm can also be used in large-scale MIMO-OFDM systems, but its performance is not ideal. With the increase of SNR (Signal to Interference plus Noise Ratio), its NMSE reduction trend is not significant. In contrast, the IISD algorithm consistently shows a better NMSE reduction trend than the ISD algorithm with increasing SNR. Therefore, the IISD algorithm outperforms both the ISD and BP algorithms. When considering an NMSE error of 10... -1 At the order of magnitude, the IISD algorithm has a 4dB gain effect over the ISD algorithm. This is due to the IISD algorithm's study on the block sparsity of the block sparse equivalent CIR. When the SNR is relatively low, the IISD algorithm is also significantly better than the ISD algorithm. This is because when the SNR is not very high, the IISD algorithm is better able to correct the support detection error caused by additive white noise than the ISD algorithm, thereby enhancing support detection and having a lower NMSE.

[0106] Compared to existing technologies, the channel estimation method disclosed in this invention, when estimating the channel of a large-scale MIMO-OFDM system using the ISD algorithm, constructs a block sparse equivalent support set of the base station's channel impulse response based on the base station's transmitted signals. The block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and is constructed from preset values ​​of the channel impulse response. During support set reconstruction, the element blocks in the block sparse equivalent support set are updated. This mechanism, which considers block sparsity, increases the robustness of support set detection, thereby improving channel estimation performance. When the length of the block sparse equivalent support set reaches a preset iteration stopping condition, the updating of the block sparse equivalent support set is stopped, and the last iteration's block sparse equivalent support set is output as an estimate of the channel impulse response. This method can explore the block sparsity of large-scale MIMO-OFDM systems, improve channel estimation performance, and output accurate CIR.

[0107] See Figure 4 , Figure 4This is a structural block diagram of a channel estimation device 100 provided in an embodiment of the present invention. The channel estimation device 100 includes:

[0108] The support set construction module 11 is used to construct a block sparse equivalent support set of the channel impulse response of the base station based on the transmitted signal of the base station; wherein, the block sparse equivalent support set contains a number of element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response.

[0109] The support set update module 12 is used to update the element blocks in the block sparse equivalent support set, and to stop updating the block sparse equivalent support set when the length of the block sparse equivalent support set is detected to be full of a preset iteration stop condition.

[0110] The channel impulse response estimation output module 13 is used to output the block sparse equivalent support set of the last iteration as the estimated value of the channel impulse response.

[0111] Specifically, the support set construction module 11 includes:

[0112] The calculation unit is used to calculate the pilot signal received by the user terminal based on the transmitted signal of the base station;

[0113] A conventional support set construction unit constructs a conventional support set of the channel impulse response based on the pilot signal received by the user terminal; wherein, the conventional support set contains several element blocks, and each element block contains several elements;

[0114] A support set equivalent unit is used to perform equivalence on the conventional support set according to the number of element blocks and the number of elements, so as to obtain the block sparse equivalent support set of the channel impulse response.

[0115] Specifically, the calculation unit is used to: acquire the pilot signal sent by the base station; and calculate the pilot signal received by the user terminal based on the pilot signal sent by the base station and the preset value of the channel impulse response.

[0116] Specifically, the support set update module 12 is used to: acquire the ordered signal of the channel impulse response and construct an ordered signal support set of the ordered signal; find the target ordered signal from the ordered signal according to a preset minimum signal difference; update the ordered signal support set with the target ordered signal as a threshold; calculate the intersection of the ordered signal support set and the block sparse equivalent support set; and update the intersection according to the index number of the element block to update the block sparse equivalent support set.

[0117] Specifically, obtaining the ordered signal of the channel impulse response includes: sorting the channel impulse response in ascending order to obtain the ordered signal of the channel impulse response.

[0118] Specifically, the iteration stopping condition is: the length of the block sparse equivalent support set is less than a preset pilot number threshold.

[0119] Specifically, the pilot number threshold is the difference between the total number of subcarriers of the base station and the number of pilots; wherein the total number of subcarriers is calculated by multiplying the total number of antennas of the base station and the number of subcarriers per antenna.

[0120] Compared to existing technologies, the channel estimation device 100 disclosed in this invention, when estimating the channel of a large-scale MIMO-OFDM system using the ISD algorithm, constructs a block sparse equivalent support set of the base station's channel impulse response based on the base station's transmitted signal. The block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response. During support set reconstruction, the element blocks in the block sparse equivalent support set are updated. This mechanism, which considers block sparsity, can increase the robustness of support set detection, thereby improving channel estimation performance. When the length of the block sparse equivalent support set is detected to meet a preset iteration stop condition, the updating of the block sparse equivalent support set is stopped, and the last iteration's block sparse equivalent support set is output as an estimate of the channel impulse response. This allows for the exploration of block sparsity in large-scale MIMO-OFDM systems, improving channel estimation performance and outputting an accurate CIR.

[0121] See Figure 5 , Figure 5 This is a structural block diagram of a channel estimation device 200 provided in an embodiment of the present invention. The channel estimation device 200 includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above-described channel estimation method embodiments, such as steps S1 to S4.

[0122] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the channel estimation device 200.

[0123] The channel estimation device 200 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the channel estimation device 200 and does not constitute a limitation on the channel estimation device 200. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the channel estimation device 200 may also include input / output devices, network access devices, buses, etc.

[0124] The processor 21 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the channel estimation device 200, connecting all parts of the channel estimation device 200 via various interfaces and lines.

[0125] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the channel estimation device 200 by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0126] If the modules / units integrated in the channel estimation device 200 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0127] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A channel estimation method, characterized in that, include: A block sparse equivalent support set for the channel impulse response of the base station is constructed based on the transmitted signal of the base station; wherein, the block sparse equivalent support set contains several element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response; Update the element blocks in the block sparse equivalent support set; When the length of the block sparse equivalent support set is detected to meet the preset iteration stop condition, the update of the block sparse equivalent support set is stopped. The block sparse equivalent support set of the last iteration is output as an estimate of the channel impulse response; The step of updating the element blocks in the block sparse equivalent support set includes: acquiring the ordered signal of the channel impulse response and constructing an ordered signal support set of the ordered signal; finding a target ordered signal from the ordered signal according to a preset minimum signal difference; updating the ordered signal support set with the target ordered signal as a threshold; calculating the intersection of the ordered signal support set and the block sparse equivalent support set; and updating the intersection according to the index number of the element blocks to update the block sparse equivalent support set.

2. The channel estimation method as described in claim 1, characterized in that, The step of constructing the block sparse equivalent support set of the channel impulse response of the base station based on the transmitted signal of the base station includes: Calculate the pilot signal received by the user terminal based on the base station's transmitted signal; The conventional support set of the channel impulse response is constructed based on the pilot signal received by the user terminal; wherein the conventional support set contains several element blocks, and each element block contains several elements; The conventional support set is equivalent to the number of element blocks and the number of elements to obtain the block sparse equivalent support set of the channel impulse response.

3. The channel estimation method as described in claim 2, characterized in that, The calculation of the user terminal's received pilot signal based on the base station's transmitted signal includes: Acquire the pilot signals sent by the base station; The pilot signal received by the user terminal is calculated based on the pilot signal sent by the base station and the preset value of the channel impulse response.

4. The channel estimation method as described in claim 1, characterized in that, The acquisition of the ordered signal of the channel impulse response includes: The channel impulse responses are sorted in ascending order to obtain ordered signals of the channel impulse responses.

5. The channel estimation method as described in claim 1, characterized in that, The iteration stopping condition is: the length of the block sparse equivalent support set is less than the preset pilot number threshold.

6. The channel estimation method as described in claim 5, characterized in that, The pilot number threshold is the difference between the total number of subcarriers of the base station and the number of pilots; wherein the total number of subcarriers is calculated by multiplying the total number of antennas of the base station and the number of subcarriers per antenna.

7. A channel estimation device, characterized in that, include: A support set construction module is used to construct a block sparse equivalent support set of the channel impulse response of the base station based on the transmitted signal of the base station; wherein, the block sparse equivalent support set contains a number of element blocks divided according to the channel impulse response, and the block sparse equivalent support set is constructed from preset values ​​of the channel impulse response; The support set update module is used to update the element blocks in the block sparse equivalent support set, and to stop updating the block sparse equivalent support set when the length of the block sparse equivalent support set meets the preset iteration stop condition. The channel impulse response estimation output module is used to output the block sparse equivalent support set of the last iteration as the estimated value of the channel impulse response; The support set update module is configured to: acquire the ordered signal of the channel impulse response and construct an ordered signal support set of the ordered signal; find the target ordered signal from the ordered signal according to a preset minimum signal difference; update the ordered signal support set with the target ordered signal as a threshold; calculate the intersection of the ordered signal support set and the block sparse equivalent support set; and update the intersection according to the index number of the element block to update the block sparse equivalent support set.

8. A channel estimation device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the channel estimation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the channel estimation method as described in any one of claims 1 to 6.

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