Low-complexity channel estimation method and device based on MIMO system

Abstract

The invention discloses a low-complexity channel estimation method and device based on an MIMO system, and the method comprises the steps: recovering a complete matrix through an approximate low-rank matrix, carrying out the singular value decomposition of the complete matrix, building a large-scale spatial multiplexing MIMO system model, carrying out the conversion processing of a received signal on a kth antenna, and obtaining a transmission signal estimation value, and performing channel matrix recovery on the transmission signal estimation value by using an approximation algorithm to obtain a channel matrix estimation value, finally performing signal detection on the channel matrix estimation value by using a zero-forcing technology, and performing iterative updating to obtain an optimal transmission signal estimation value, thereby effectively reducing the algorithm complexity and effectively reducing the calculation amount. The estimated channel matrix is effectively equivalent to the complete channel matrix, and the channel matrix is updated through iterative updating, so that the dynamic change of the channel can be effectively tracked, and the final demodulation signal precision is ensured.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a low-complexity channel estimation method and device based on a MIMO system. Background Art

[0002] With the growth of wireless communication demand and technological advancement, Multiple-Input Multiple-Output (MIMO) systems have attracted widespread attention because they can significantly improve channel capacity without increasing the consumption of additional spectrum resources. Compared with the traditional Single-Input Single-Output (Single-Input Single-Output) system, MIMO technology effectively improves data transmission rate and reliability by utilizing the spatial diversity between multiple transmit antennas and receive antennas. However, in practical applications, especially under high channel capacity requirements or in the face of complex and dynamically changing wireless environments, how to ensure efficient and stable communication has become one of the problems that need to be solved urgently.

[0003] In order to overcome problems such as frequency selective fading caused by multipath effects, traditional MIMO systems use diversity technology to enhance signal stability. This technology allows the entire communication link to maintain good performance even when severe attenuation occurs on one or more paths. However, this method relies on highly accurate estimation of the channel state, especially in large-scale MIMO systems, where the channel matrix becomes extremely large and complex due to the interaction between a large number of antennas. Using conventional methods to perform accurate channel estimation often faces a huge computational burden, which is not only time-consuming but also costly.

[0004] In addition, for large-scale MIMO systems, accurately estimating all possible channel parameters is a very challenging task. Although some algorithms have attempted to reduce the amount of computation, they usually sacrifice a certain degree of accuracy. In practical environments, the channel conditions between the transmitter and receiver are constantly changing, which requires that any effective solution must be flexible enough to cope with these changes. Recent studies have shown that advanced methods such as deep learning can extract key features from complex channel models and approximate them with a smaller number of parameters. Although this direction shows great potential, there is no consensus on how to optimally select or construct such a low-rank structure.

[0005] For the channel estimation problem mentioned above, the development of signal detection technology is also crucial. It is not only related to whether the received information can be correctly decoded, but also directly affects the bit error rate level of the overall system. Especially when considering factors such as inter-carrier interference (ICI), finding an effective detection strategy that is compatible with high-performance channel estimation algorithms has become one of the key areas of current research. Summary of the invention

[0006] The purpose of this application is to overcome the existing technical defects and provide a low-complexity channel estimation method and device based on a MIMO system. By updating the channel matrix through iterative updating, the dynamic changes of the channel can be effectively tracked to ensure the final demodulation signal accuracy.

[0007] The purpose of this application is achieved through the following technical solutions:

[0008] In a first aspect, the present application proposes a low-complexity channel estimation method based on a MIMO system, the method comprising:

[0009] Recover the full matrix by approximating the low-rank matrix;

[0010] After the singular value decomposition of the complete matrix, it is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A;

[0011] A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is transformed to obtain the transmission signal estimation value;

[0012] Using an approximate algorithm to recover the channel matrix of the transmission signal estimate to obtain a channel matrix estimate;

[0013] The channel matrix estimation value is subjected to signal detection using the zero-forcing technique and then iteratively updated to obtain the optimal transmission signal estimation value.

[0014] In a possible implementation, a large-scale spatial multiplexing MIMO system model is established, and the step of converting a received signal on the kth antenna to obtain a transmission signal estimation value includes:

[0015] A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Yk For: Y k =H k X k +Z k , where Z k represents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver,

[0016] The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user

[0017] In a possible implementation manner, without considering noise interference, the estimated value of the transmission signal received by the kth antenna is for:

[0018] In a possible implementation, the channel matrix estimate is: Where 0<ε<1 represents the approximate base value, It means calculating the value of x when f(x) takes the minimum value;

[0019] Channel matrix estimate The iterative equation is: Where 0<μ<1 is the error parameter.

[0020] In a possible implementation, the step of performing signal detection on the channel matrix estimation value by using a zero-forcing technique and then iteratively updating to obtain an optimal transmission signal estimation value includes:

[0021] The channel matrix estimate is subjected to signal detection using zero-forcing technology to obtain the transmit signal estimate. W a The weighting matrix used for the zero-forcing technique;

[0022] By transmitting the signal to estimate the value renew Information transmitted by users in X k , and obtain the optimal transmit signal estimate when the preset number of iterations is reached.

[0023] In a possible implementation, the method further includes:

[0024] When the rank of the complete matrix is ​​less than the preset threshold, the operation of the complete matrix is ​​converted into a rank minimization problem: minrank(A),A ij =B ij ,(i,j∈C), where A is a matrix of m×n dimensions, B ijrepresents the known part of the matrix, C represents the coordinate set of the matrix elements, rank(·) represents the rank operation of the matrix, and min(·) represents the minimum operation;

[0025] Based on the principle of matrix linear operation, the rank minimization problem is transformed into: minrank(A), D(A) = b, where D(·) represents the linear operator and b represents the decision vector.

[0026] In a possible implementation, the method further includes:

[0027] In signal processing, minrank(A),A ij =B ij ,(i,j∈T) is equivalent to min||A|| * ,A i,j =B i,j , where ||A|| * represents the sum of the singular values ​​of the matrix A, A i,j Represents the equivalent matrix of matrix A.

[0028] Based on the noise, min||A|| * ,A i,j =B i,j The equivalent expression is, where δ represents noise, It means that the result of the F norm operation on the complete matrix A is squared, and the unconstrained regularization operation is obtained. Where μ represents the error, satisfying 0<μ<1, A eq represents an equivalent matrix.

[0029] In a possible implementation manner, when the number of columns of the complete matrix is ​​n and the rank is r, the number of rows m of the complete matrix satisfies: m ≥ 2nr-r 2 .

[0030] In a second aspect, an embodiment of the present application further proposes a low-complexity channel estimation device based on a MIMO system, the device comprising:

[0031] A recovery module, used to recover the complete matrix by approximating the low-rank matrix;

[0032] The singular value decomposition module is used to perform singular value decomposition on the complete matrix, which is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A;

[0033] The conversion processing module is used to establish a large-scale spatial multiplexing MIMO system model and convert the received signal on the kth antenna to obtain the transmission signal estimation value;

[0034] A channel matrix recovery module is used to recover the channel matrix of the transmission signal estimation value using an approximate algorithm to obtain a channel matrix estimation value;

[0035] The iterative update module is used to perform signal detection on the channel matrix estimation value by using the zero-forcing technique and then iteratively update it to obtain the optimal transmission signal estimation value.

[0036] In a possible implementation, the conversion processing module is further used to:

[0037] A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Y k For: Y k =H k X k +Z k , where Z k represents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver,

[0038] The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user

[0039] The above-mentioned main scheme of the present application and its further options can be freely combined to form multiple schemes, all of which are schemes that can be adopted and claimed for protection in the present application; and in the present application, (non-conflicting options) options and other options can also be freely combined. After understanding the scheme of the present application, those skilled in the art can understand that there are multiple combinations based on the prior art and common knowledge, all of which are technical schemes to be protected by the present application, and they are not exhaustively listed here.

[0040] The present application discloses a low-complexity channel estimation method and device based on a MIMO system. First, the complete matrix is ​​restored by approximating a low-rank matrix, and a large-scale spatial multiplexing MIMO system model is established after singular value decomposition of the complete matrix. The received signal on the root antenna is transformed and processed to obtain a transmission signal estimation value. The transmission signal estimation value is restored by using an approximate algorithm to obtain a channel matrix estimation value. Finally, the channel matrix estimation value is iteratively updated after signal detection using a zero-forcing technique, thereby obtaining an optimal transmission signal estimation value. The algorithm complexity is effectively reduced and the amount of calculation is effectively reduced. The estimated channel matrix is ​​effectively equivalent to a complete channel matrix, and the channel matrix is ​​updated by iterative updating, which can effectively track the dynamic changes of the channel and ensure the final demodulation signal accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic flow chart of a low-complexity channel estimation method based on a MIMO system proposed in an embodiment of the present application is shown.

[0043] Figure 2 A schematic diagram of the MIMO system model proposed in an embodiment of the present application is shown.

[0044] Figure 3 The BER performance simulation diagram under the quasi-static Rayleigh channel proposed in the embodiment of the present application is shown.

[0045] Figure 4 A simulation diagram of the BER performance under a dynamic Rayleigh channel proposed in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0047] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0048] In the prior art, a multiple-input multiple-output system has multiple transmitting antennas and multiple receiving antennas, which can make full use of spatial resources and increase the channel capacity exponentially without consuming additional spectrum resources and antenna transmission power. Given a high-capacity wireless channel, it is still necessary to explore high-speed transmission and high-reliability communication technologies. In a harsh channel environment, traditional diversity technology uses multiple antennas to send data streams, which can effectively improve the transmission rate. Multipath channels have characteristics such as frequency selective fading and multipath delay. The purpose of using diversity technology is to convert multipath channels into stable channels without catastrophic fading like Gaussian additive white noise channels, so as to ensure the channel estimation accuracy in complex transmission environments. However, traditional channel estimation algorithms are difficult to ensure their channel estimation accuracy, and when traditional algorithms process high-dimensional and large-scale channel matrices, the amount of calculation doubles and the algorithm complexity is too high, making it difficult to apply in practice in engineering. Therefore, a high-precision and low-complexity channel estimation algorithm is required to estimate channel parameters to ensure that the communication system has high reliability and high transmission rate.

[0049] Therefore, in order to solve the above technical problems, the embodiment of the present application proposes a low-complexity channel estimation method and device based on a MIMO system, which uses an approximate algorithm to find an approximate matrix to minimize the difference between the matrix and the actual channel matrix, and provide reliable channel estimation results for large-scale MIMO communication systems, thereby improving communication performance and system capacity. In addition, the channel matrix estimation results are updated in real time through an iterative algorithm, ensuring that the algorithm can provide high-precision estimation results in a high-dynamic environment, and finally combined with a low-complexity signal detection algorithm to effectively ensure the final demodulation signal accuracy, which is described in detail below.

[0050] Please refer to Figure 1 , Figure 1 A schematic flow chart of a low-complexity channel estimation method based on a MIMO system proposed in an embodiment of the present application is shown. The method is applied to a MIMO system and includes:

[0051] Step S1, restoring the complete matrix by approximating the low-rank matrix;

[0052] In large-scale MIMO systems, the channel matrix often has a low-rank characteristic, that is, the rank of the matrix is ​​much smaller than its dimension. A low-rank matrix is ​​used to approximate the original complete matrix. First, a complete matrix A is assumed to have a very small rank, m and n are the number of rows and columns of matrix A, and then the complete matrix A is restored by a low-rank matrix. When the rank of matrix A is very small, the operation of solving the complete matrix A can be converted into a rank minimization problem and satisfy the constraints of the known part. That is, the complete matrix A is restored by an approximate low-rank matrix. When the rank of the complete matrix A is very small, the operation of solving the complete matrix A can be converted into a rank minimization problem.

[0053] The method further includes:

[0054] When the rank of the complete matrix is ​​less than the preset threshold, the operation of the complete matrix is ​​converted into a rank minimization problem: minrank(A),A ij =B ij ,(i,j∈C), where A is a matrix of m×n dimensions, B ij represents the known part of the matrix, C represents the coordinate set of the matrix elements, rank(·) represents the rank operation of the matrix, and min(·) represents the minimum operation;

[0055] Based on the principle of matrix linear operation, the rank minimization problem is transformed into: minrank(A), D(A) = b, where D(·) represents the linear operator and b represents the decision vector.

[0056] Step S2, after performing singular value decomposition on the complete matrix, is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A;

[0057] A low-rank matrix can carry the main characteristic information of the complete matrix, even if its dimension is much smaller than the complete matrix. For example, for a matrix A. For a complete matrix A, if only the elements of the first row and the first column are 1, and the other positions are 0, then the A matrix cannot be used as a complete matrix because the rank is 1 (very small). When the number of columns of the complete matrix is ​​n and the rank is r, the number of rows m of the complete matrix satisfies: m ≥ 2nr-r 2 .

[0058] The singular value decomposition (SVD) of the complete matrix A can be equivalent to: where σ k is an m×m unitary matrix; uk is an m×n order non-negative real diagonal matrix; is a unitary matrix of order n×n; u k The elements on the diagonal are the singular values ​​of the complete matrix A.

[0059] In the subsequent iteration process, singular value decomposition is performed based on the channel estimation results obtained in the previous round of iteration, and the singular values ​​are used as the basis for a new iteration, which helps to quickly converge to a channel state that is closer to the actual situation, especially in a highly dynamically changing wireless environment.

[0060] The method further includes:

[0061] In signal processing, minrank(A),A ij =B ij ,(i,j∈T) is equivalent to min||A|| * ,A i,j =B i,j , where ||A|| * represents the sum of the singular values ​​of the matrix A, A i,j Represents the equivalent matrix of matrix A.

[0062] Based on the noise, min||A|| * ,A i,j =B i,j The equivalent expression is, where δ represents noise, It means that the result of the F norm operation on the complete matrix A is squared, and the unconstrained regularization operation is obtained. Where μ represents the error, satisfying 0<μ<1, A eq represents an equivalent matrix.

[0063] In the case of noise, by introducing the nuclear norm regularization term, the original matrix can be effectively restored and the impact of noise can be reduced.

[0064] Step S3: Establish a large-scale spatial multiplexing MIMO system model, and transform the received signal on the kth antenna to obtain a transmission signal estimation value.

[0065] Step S3 includes:

[0066] A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Y k For: Y k =H k X k +Z k , where Z krepresents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver,

[0067] The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user

[0068] Figure 2 A schematic diagram of the MIMO system model proposed in an embodiment of the present application is shown. The transmitting end converts the original information into a form suitable for transmission and transmits it through a channel. At the receiving end, the received signal needs to be processed. According to the estimation of the channel matrix, the received signal is detected and decoded to restore the original information and finally output the original information.

[0069] In a MIMO system, both the transmitter and receiver are equipped with multiple antennas. In an actual communication environment, the signal reaches the receiver through multiple paths, and the propagation characteristics of each path are different, which will cause mutual interference between signals. This multipath effect can be represented by a multipath channel matrix. In addition to the multipath effect, the signal is also affected by additive white Gaussian noise (AWGN), which is superimposed on the signal during the transmission process.

[0070] Assume a massive MIMO system with N antennas on both the sender and the receiver. In this case, each user's signal can be transmitted through multiple independent spatial channels, thus achieving higher spectral efficiency. The received signal Y on the kth antenna k Expressed as: Y k =H k X k +Z k where k = 1, 2, ..., K; Z k represents the additive white Gaussian noise matrix; X k represents the information transmitted by the user, including pilot information; H represents the Rayleigh fading channel matrix between the sender and the receiver. The least squares method (LS) is then used to estimate the channel matrix.

[0071] Without considering noise interference, the estimated value of the transmission signal received by the kth antenna is for:

[0072] Step S4: Use an approximate algorithm to recover the channel matrix of the transmission signal estimate to obtain a channel matrix estimate.

[0073] The channel matrix estimate is: Where 0<ε<1 represents the approximate base value, It means calculating the value of x when f(x) takes the minimum value;

[0074] Channel matrix estimate The iterative equation is: Where 0<μ<1 is the error parameter.

[0075] The channel matrix estimation on a single receive antenna is expressed as: in It means calculating the value of x that makes f(x) take the minimum value. Representation Matrix The a-norm of , a is greater than 0. When a approaches 0, The result of the operation is closer to the matrix rank.

[0076] Since the traditional LS channel estimation algorithm ignores the influence of noise interference, it will lead to deterioration of channel estimation accuracy in a low signal-to-noise ratio environment. Therefore, we consider using a low-complexity channel estimation method to restore the channel matrix, remove the influence of noise interference, and use an approximate algorithm to restore the channel matrix.

[0077] Step S5: Perform signal detection on the channel matrix estimation value by using the zero-forcing technique and then iteratively update it to obtain the optimal transmission signal estimation value.

[0078] Zero-Forcing (ZF) technology is used in MIMO systems for signal detection to eliminate or reduce interference between multiple antennas caused by simultaneous transmission.

[0079] Step S5 includes:

[0080] The channel matrix estimate is subjected to signal detection using zero-forcing technology to obtain the transmit signal estimate. W a The weighting matrix used for the zero-forcing technique;

[0081] By transmitting the signal to estimate the value renew Information transmitted by users in X k , and obtain the optimal transmit signal estimate when the preset number of iterations is reached.

[0082] After obtaining the iterative channel matrix, the signal detection technology is used to eliminate the influence of inter-subcarrier interference, and the channel matrix estimation value is obtained after each iteration. Perform signal detection calculations and use zero-forcing signal detection technology. The zero-forcing technology uses a weighted matrix to eliminate interference, thereby obtaining an estimated value of the transmitted signal. renew X in k , in order to update the channel matrix estimate After completing the set number of iterations, the receiver obtains the final estimated value of the transmitted signal.

[0083] In a possible embodiment, Table 1 shows the simulation parameters set:

[0084] Table 1

[0085] parameter Parameter settings Number of multipath channels 50 Number of antennas K 10 Algorithm iteration number a 30 Number of antenna directions 10 Simulation Tools Matlab2022a

[0086] The MIMO system uses the above simulation parameters and 5000 Monte Carlo simulations to simulate the bit error rate (BER) performance of using traditional channel estimation algorithms and low-complexity large-scale channel estimation algorithms in the MIMO system. The performance under Rayleigh fading channels is mainly simulated.

[0087] Where EbN0 represents the sensitivity, Figure 3 The BER performance simulation diagram under the quasi-static Rayleigh channel proposed in the embodiment of the present application is shown. After adding the multipath algorithm (i.e., the channel estimation and equalization algorithm), there is a significant performance improvement under the quasi-static Rayleigh fading channel. This is because the low-complexity channel estimation algorithm can effectively eliminate the influence of noise interference, and the signal detection algorithm can effectively eliminate the influence of inter-subcarrier interference.

[0088] Figure 4 The BER performance simulation diagram under the dynamic Rayleigh channel proposed in the embodiment of the present application is shown. After adding the multipath algorithm (i.e., channel estimation and equalization algorithm), there is a significant performance improvement under the dynamic Rayleigh fading channel. At this time, the relative moving speed of the sender and receiver is set to 200km / h. It can be seen that the proposed low-complexity channel estimation algorithm can effectively improve the bit error rate performance when applied to the MIMO system, because it effectively updates the channel matrix through an iterative algorithm and implements the dynamic changes of the tracking channel.

[0089] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0090] First, by using an approximate low-rank channel matrix instead of the actual high-dimensional channel matrix, the amount of data to be processed and the corresponding computing tasks are significantly reduced.

[0091] Second, the simplified channel estimation method not only reduces the computing resources required for local processing, but also reduces the demand for network bandwidth.

[0092] Third, although an approximate model is used, a deep understanding of the channel characteristics and appropriate mathematical modeling can ensure that the estimated results are still accurate enough.

[0093] Fourth, combined with effective signal detection technology, this solution can eliminate mutual interference between subcarriers and other forms of multipath fading effects to a certain extent, thereby improving the overall robustness of the system and its ability to combat harsh wireless environments.

[0094] Fifth, the iterative update mechanism is used to continuously adjust and improve the current channel model so that the system can better follow the changes in the actual physical environment. This is crucial to achieving long-term stable and reliable wireless connections.

[0095] Sixth, the data stream recovered from the receiving end is as close as possible to the content originally sent by the sending end. By optimizing the channel estimation process and combining an efficient decoding algorithm, the present invention helps achieve this goal, ensuring a high-quality service experience even under complex and changing propagation conditions.

[0096] A possible implementation of a low-complexity channel estimation device based on a MIMO system is provided below, which is used to execute the various execution steps and corresponding technical effects of the low-complexity channel estimation method shown in the above embodiment and possible implementation. The device includes:

[0097] A recovery module, used to recover the complete matrix by approximating the low-rank matrix;

[0098] The singular value decomposition module is used to perform singular value decomposition on the complete matrix, which is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A;

[0099] The conversion processing module is used to establish a large-scale spatial multiplexing MIMO system model and convert the received signal on the kth antenna to obtain the transmission signal estimation value;

[0100] A channel matrix recovery module is used to recover the channel matrix of the transmission signal estimation value using an approximate algorithm to obtain a channel matrix estimation value;

[0101] The iterative update module is used to perform signal detection on the channel matrix estimation value by using the zero-forcing technique and then iteratively update it to obtain the optimal transmission signal estimation value.

[0102] In a possible implementation, the conversion processing module is further used to:

[0103] A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Y k For: Y k =H k X k +Z k , where Z k represents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver,

[0104] The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user

[0105] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A low-complexity channel estimation method based on a MIMO system, characterized in that: The method comprises: Recover the full matrix by approximating the low-rank matrix; After the singular value decomposition of the complete matrix, it is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A; A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is transformed to obtain the transmission signal estimation value; Using an approximate algorithm to recover the channel matrix of the transmission signal estimate to obtain a channel matrix estimate; The channel matrix estimation value is subjected to signal detection by using the zero-forcing technique and then iteratively updated to obtain the optimal transmission signal estimation value.

2. The low complexity channel estimation method according to claim 1, characterized in that: The steps of establishing a large-scale spatial multiplexing MIMO system model and converting the received signal on the kth antenna to obtain the estimated value of the transmitted signal include: A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Y k For: Y k =H k X k +Z k , where Z k represents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver, The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user 3. The low complexity channel estimation method according to claim 2, characterized in that: Without considering noise interference, the estimated value of the transmission signal received by the kth antenna is for:

4. The low complexity channel estimation method according to claim 3, characterized in that: The channel matrix estimate is: Where 0<ε<1 represents the approximate base value, It means calculating the value of x when f(x) takes the minimum value; Channel matrix estimate The iterative equation is: Where 0<μ<1 is the error parameter.

5. The low complexity channel estimation method according to claim 4, characterized in that: The step of performing signal detection on the channel matrix estimation value by using a zero-forcing technique and then iteratively updating the channel matrix estimation value to obtain the optimal transmission signal estimation value comprises: The channel matrix estimate is subjected to signal detection using zero-forcing technology to obtain the transmit signal estimate. W a The weighting matrix used for the zero-forcing technique; By transmitting the signal to estimate the value renew Information transmitted by users in X k , and obtain the optimal transmit signal estimate when the preset number of iterations is reached.

6. The low complexity channel estimation method according to claim 1, characterized in that: The method further comprises: When the rank of the complete matrix is ​​less than the preset threshold, the operation of the complete matrix is ​​converted into a rank minimization problem: min rank(A),A ij =B ij ,(i,j∈C), where A is a matrix of m×n dimensions, B ij represents the known part of the matrix, C represents the coordinate set of the matrix elements, rank(·) represents the rank operation of the matrix, and min(·) represents the minimum operation; Based on the principle of matrix linear operation, the rank minimization problem is transformed into: min rank(A), D(A) = b, where D(·) represents the linear operator and b represents the decision vector.

7. The low complexity channel estimation method according to claim 1, characterized in that: The method further comprises: In signal processing, min rank(A),A ij =B ij ,(i,j∈T) is equivalent to min||A|| * ,A i,j =B i,j , where ||A|| * represents the sum of the singular values ​​of the matrix A, A i,j represents the equivalent matrix of matrix A; Based on the noise, min||A|| * ,A i,j =B i,j The equivalent expression is, where δ represents noise, It means that the result of the F norm operation on the complete matrix A is squared, and the unconstrained regularization operation is obtained. Where μ represents the error, satisfying 0<μ<1, A eq represents an equivalent matrix.

8. The low complexity channel estimation method according to claim 1, wherein: When the number of columns of the complete matrix is ​​n and the rank is r, the number of rows m of the complete matrix satisfies: m≥2nr-r 2 .

9. A low-complexity channel estimation device based on a MIMO system, characterized in that: The device comprises: A recovery module, used to recover the complete matrix by approximating the low-rank matrix; The singular value decomposition module is used to perform singular value decomposition on the complete matrix, which is equivalent to: where A is the complete matrix, σ k is an m×m unitary matrix, u k is an m×n-order non-negative real diagonal matrix, is an n×n unitary matrix, u k are the singular values ​​of the complete matrix A; The conversion processing module is used to establish a large-scale spatial multiplexing MIMO system model and convert the received signal on the kth antenna to obtain the transmission signal estimation value; A channel matrix recovery module is used to recover the channel matrix of the transmission signal estimation value using an approximate algorithm to obtain a channel matrix estimation value; The iterative update module is used to perform signal detection on the channel matrix estimation value by using the zero-forcing technique and then iteratively update it to obtain the optimal transmission signal estimation value.

10. The low complexity channel estimation device according to claim 9, characterized in that: The conversion processing module is also used to: A large-scale spatial multiplexing MIMO system model is established, and the received signal on the kth antenna is processed using the least squares channel estimation algorithm to obtain the channel estimation value of the kth receiving antenna. The receiving signal Y k For: Y k =H k X k +Z k , where Z k represents the additive white Gaussian noise matrix, X k represents the information transmitted by the user, H represents the Rayleigh fading channel matrix between the sender and the receiver, The transmission signal estimation value is obtained based on the received signal and the information transmitted by the user

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