A signal detection method and MIMO system

Abstract

The present invention discloses a signal detection method and a MIMO system, belonging to the field of wireless communication technology. The method converts the problem of solving the minimum mean square error into the problem of solving a system of linear equations through a detection method based on discrete estimation iteration, and can obtain good detection performance in a large-scale MIMO system. The method also has low computational complexity and is suitable for large-scale MIMO systems. At the same time, in order to accelerate the convergence speed of the Barzilai-Borwein iterative signal detection method and improve the performance of the detection method, the present invention proposes to improve the initial value of the modified Barzilai-Borwein, and at the same time, reasonably select the step size, and use the improved modified Barzilai-Borwein iterative algorithm to detect the received signal matrix, and obtain the estimated value of the transmitted signal, thereby improving the error performance and convergence speed of the detection method.

Description

Technical Field

[0001] The present invention relates to a signal detection method and a MIMO system, belonging to the technical field of wireless communications. Background Art

[0002] Signal detection is the process of extracting the signal containing interference noise at the receiving end and then restoring the original signal. It is of great significance in the field of communications.

[0003] Massive-Multiple-Input Multiple-Output (M-MIMO) system is one of the key technologies of the fifth-generation mobile communication system. By equipping the transmitter and receiver with a large number of antennas, the transmitter and receiver can use multiple antennas to transmit signals during the communication process, significantly improving the channel capacity, data transmission rate, spectrum efficiency and communication quality of the communication system.

[0004] However, due to the massive increase in the number of antennas, many high-performance methods suitable for traditional MIMO systems are no longer applicable to massive MIMO systems because these methods often bring high complexity when applied to massive MIMO systems. Therefore, how to achieve low detection complexity while maintaining good performance has become an urgent problem to be solved.

[0005] Traditional signal detection methods can be broadly categorized into two types based on their computational characteristics: nonlinear and linear. Nonlinear detection, such as maximum likelihood detection (ML), uses nonlinear operations to process information, which can achieve excellent system performance. However, its computational complexity is too high, making it unsuitable for large-scale MIMO systems. In contrast, linear detection uses linear operations to process information, offering simple implementation and low computational complexity. Key examples include zero-forcing detection (ZF), matched filtering (MF), and minimum mean-squared error (MMSE).

[0006] Among them, the MMSE algorithm can achieve near-optimal linear signal detection performance, but the MMSE algorithm requires complex matrix inversion operations, which limits the application of the algorithm. In order to reduce the computational complexity, converting MMSE detection into a very effective technology for solving a system of linear equations is a very effective technology.

[0007] In recent years, the application of iterative algorithms to solve detection problems in large-scale MIMO systems has become increasingly widespread. Liu Xiaoxiang et al. proposed a signal detection method that uses the Barzilai-Borwein iterative algorithm to perform signal detection in large-scale MIMO systems. Although this scheme solves the problem of high complexity in inverting the MMSE matrix, it still has problems such as high bit error rate and slow convergence speed (Liu Xiaoxiang, Zhang Jing. Low-complexity large-scale MIMO signal detection algorithm based on Barzilai-Borwein iteration [J]. Systems Engineering and Electronics, 2018, 40(08): 1861-1865.).

[0008] Jin et al. proposed a signal detection method that combines the steepest descent method with the Barzilai-Borwein algorithm to form a modified Barzilai-Borwein (CBB) iterative method. CBB is then used for signal detection. Practice has shown that although this scheme improves detection performance to a certain extent, it still needs to be further improved in terms of bit error rate and convergence speed (Jin J, Zhang Z, You X, et al. Massive MIMO Detection Based on Barzilai-Borwein Algorithm[C]. 2018 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2018: 152-157.). Summary of the Invention

[0009] In order to further improve the speed and detection performance of signal detection in a large-scale MIMO system, the present invention provides a signal detection method and a MIMO system. The technical solution is as follows:

[0010] A first object of the present invention is to provide a signal detection method for a MIMO system, comprising:

[0011] Step 1: constructing a minimum mean square error signal detection algorithm based on the channel gain matrix of the massive MIMO system;

[0012] Step 2: Convert the problem of solving the minimum mean square error signal detection algorithm into the problem of solving a system of linear equations;

[0013] Step 3: Convert the problem of solving the linear equations into a problem of iterative solution by a solution algorithm, that is, reconstruct the detection problem into a problem of iterative solution by a solution algorithm;

[0014] Step 4: Use the improved modified Barzilai-Borwein algorithm to detect the received signal matrix and obtain the estimated value of the transmitted signal The improved modified Barzilai-Borwein algorithm improves the initial value and the step size.

[0015] Optionally, the initial value of the improved modified Barzilai-Borwein algorithm is:

[0016]

[0017] b=H H y

[0018] Where N represents the number of receiving antennas, K represents the number of transmitting antennas, y represents the received signal vector, and H represents the channel gain matrix.

[0019] Optionally, the step size of the improved modified Barzilai-Borwein algorithm is:

[0020]

[0021]

[0022] in, represents the iterative solution obtained by the t-th iteration, and A represents the detection matrix of the minimum mean square error signal detection algorithm.

[0023] Optionally, the minimum mean square error signal detection algorithm constructed in step 1 is:

[0024]

[0025] Where y represents the received signal vector, H represents the channel gain matrix, σ 2 represents the noise variance, K represents the number of transmitting antennas, Represents the estimated value of the transmitted signal.

[0026] Optionally, the second step includes:

[0027] The minimum mean square error detection algorithm is converted into an algorithm iterative solution problem using the following formula:

[0028]

[0029]

[0030] Wherein A represents the detection matrix of the minimum mean square error signal detection algorithm; b represents the matched filter output of the received signal.

[0031] Optionally, the process of calculating the t+1th iterative signal detection estimation value in step 4 includes:

[0032]

[0033] h (t) =Ar (t)

[0034]

[0035] Among them, μ (t) represents a variable step size, θ represents a multiple of the step size, represents the iterative solution obtained at the tth iteration, represents the residual vector.

[0036] Optionally, the channel gain matrix H is a Rayleigh fading channel gain matrix.

[0037] A second object of the present invention is to provide a MIMO signal detection device, comprising:

[0038] a signal receiving unit, configured to receive a signal;

[0039] a signal detection unit, detecting the received signal using the signal detection method of the MIMO system described above;

[0040] The signal output unit is used to output the detection result of the signal detection unit.

[0041] The third object of the present invention is to provide a MIMO communication system, comprising a transmitter and a receiver, wherein the receiver comprises the above-mentioned signal detection device.

[0042] The fourth object of the present invention is to provide a MIMO communication method, wherein the signal sent by the transmitting end is received by the receiving end through the channel transmission, and is characterized in that when the receiving end receives the signal, the received signal is detected using the signal detection method of the above-mentioned MIMO system.

[0043] The beneficial effects of the present invention are:

[0044] The signal detection method of the present invention utilizes an improved modified Barzilai-Borwein iterative algorithm to detect received signals. During the detection process, the signal detection problem is converted into solving a system of linear equations. Experimental results demonstrate that, compared with existing signal detection methods, the present invention can achieve better detection performance in large-scale MIMO systems. By improving the initial value and step size of the modified Barzilai-Borwein iterative algorithm, the present invention also has lower computational complexity, improving the error rate performance and convergence speed of the detection method. Therefore, the present invention is suitable for large-scale MIMO systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 This is a comparison chart of the bit error rates of Barzilai-Borwein, modified Barzilai-Borwein (CBB) iteration method, improved modified Barzilai-Borwein, and MMSE under the conditions of Rayleigh fading channel, 32QAM modulation mode, 128 base station side receiving antennas, and 32 users.

[0047] Figure 2 This is a comparison chart of the bit error rates of Barzilai-Borwein, modified Barzilai-Borwein (CBB) iteration method, improved modified Barzilai-Borwein, and MMSE under the conditions of Rayleigh fading channel, 64QAM modulation, 128 base station side receiving antennas, and 32 users.

[0048] Figure 3 This is a comparison chart of the bit error rates of Barzilai-Borwein, modified Barzilai-Borwein (CBB) iteration method, improved modified Barzilai-Borwein, and MMSE under the conditions of Rayleigh fading channel, 32QAM modulation, 64 base station side receiving antennas, and 16 users. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0050] First, the basic theoretical knowledge involved in this invention is introduced:

[0051] Example 1:

[0052] This embodiment provides a signal detection method for a MIMO system, including the following steps:

[0053] Step 1: constructing a minimum mean square error signal detection algorithm based on the channel gain matrix of the massive MIMO system;

[0054] Step 2: Convert the problem of solving the minimum mean square error signal detection algorithm into the problem of solving a system of linear equations;

[0055] Step 3: Convert the problem of solving the linear equations into an iterative solution problem of the solution algorithm, that is, reconstruct the detection problem into an iterative solution problem of the solution algorithm;

[0056] Step 4: Use the improved modified Barzilai-Borwein algorithm to detect the received signal matrix and obtain the estimated value of the transmitted signal The improved modified Barzilai-Borwein algorithm improves the initial value and the step size.

[0057] Example 2:

[0058] This embodiment provides a discrete estimation iterative signal detection method in a massive MIMO system. The system model applicable to the method is:

[0059] y C =H C x C +n C

[0060] In the above formula is the signal received by the base station side, is the signal sent by the transmitter, N represents the number of receiving antennas, K represents the number of transmitting antennas, is additive white Gaussian noise, H C represents a Rayleigh fading channel.

[0061] In order to analyze and process the data, the complex channel model is converted into a real channel model. Each model can be converted into:

[0062]

[0063]

[0064]

[0065]

[0066] in represents the real part of a complex matrix or vector, Represents the imaginary part of a complex matrix or vector.

[0067] The signal transmission can then be rewritten as:

[0068] y=Hx+n

[0069] The signal detection method of this embodiment includes the following steps:

[0070] Step 1: Construct the minimum mean square error detection algorithm based on the channel gain matrix H;

[0071] The minimum mean square error detection algorithm is constructed using the following formula (1);

[0072]

[0073] In formula (1), y represents the received signal vector, H represents the channel gain matrix, σ 2 represents the noise variance, I K The unit matrix representing the number of transmitting antennas, Represents the estimated value of the transmitted signal.

[0074] Step 2: Convert the minimum mean square error detection algorithm into a problem of solving a system of linear equations;

[0075] The minimum mean square error detection algorithm is converted into a problem of solving a system of linear equations using the following formula (2):

[0076]

[0077] Where A represents the detection matrix of the minimum mean square error detection algorithm MMSE; b represents the matched filter output of the received signal;

[0078] Step 3: Convert the problem of solving the linear equations into the problem of solving the algorithm iterative solution, that is, reconstruct the signal detection problem into the problem of solving the algorithm iterative solution;

[0079] Formula (3) will use the Barzilai-Borwein algorithm to solve the iterative solution problem

[0080]

[0081] μ (t-1) represents the step size of the t-1th iteration of the steepest descent method, represents the residual vector.

[0082] Combining the steepest descent method with the Barzilai-Borwein algorithm (CBB) yields:

[0083]

[0084] Step 4: Optimize the modified Barzilai-Borwein iterative algorithm, improve the initial value of the modified Barzilai-Borwein (CBB), and reasonably select the step size;

[0085] Formula (4) optimizes the modified Barzilai-Borwein iterative algorithm

[0086]

[0087] h (t)=Ar (t)

[0088]

[0089] The first formula of formula (4) is the step length formula, where θ represents the multiple of the step length. (t) Can be obtained by r (t) It is determined that the third formula is the improved modified Barzilai-Borwein iterative process.

[0090] The initial value of the iterative algorithm does not affect the convergence of the algorithm, but the initial value selection has a certain impact on the convergence speed and detection accuracy of the iterative algorithm. Generally speaking, when the initial value is a zero vector, the algorithm converges very slowly. In order to achieve faster convergence speed, this embodiment selects the Richardson initial value as:

[0091]

[0092] Step 5: Use the improved modified Barzilai-Borwein algorithm to detect the received signal matrix y and obtain the estimated value of the transmitted signal

[0093] Calculate the signal detection estimate for the t+1th iteration:

[0094]

[0095] In step 5, two estimated solutions are used to obtain a more accurate iterative detection algorithm. The iterative detection process is as follows:

[0096]

[0097]

[0098] In order to make the purpose, technical solution and advantages of the present invention clearer, a comparative experiment is conducted between the signal detection method of the present invention and the existing signal detection method to demonstrate the superiority of the signal detection method based on the improved modified Barzilai-Borwein iterative detection algorithm of the present invention in terms of complexity and bit error rate performance.

[0099] The detection methods used for simulation are respectively a signal detection method based on MMSE, a detection method based on Barzilai-Borwein, a signal detection method based on a modified Barzilai-Borwein (CBB) iterative method, and a signal detection method based on an improved modified Barzilai-Borwein iterative detection algorithm of the present invention.

[0100] The MMSE detection algorithm is a classic linear detection algorithm that demonstrates excellent bit error rate performance in large-scale MIMO systems. The Barzilai-Borwein iterative detection algorithm transforms the minimum mean square error problem into a system of linear equations and solves it using the Barzilai-Borwein iterative algorithm. The improved modified Barzilai-Borwein detection algorithm refines the initial values ​​of the modified Barzilai-Borwein while selecting a reasonable step size. Both algorithms reconstruct the minimum mean square error detection problem, achieving low complexity while exhibiting bit error rate performance close to that of the MMSE detection algorithm.

[0101] The experimental results are as follows Figure 1 、 2 As shown in , 3, it can be seen from the simulation curve that, compared with several other signal detection methods, the present invention can effectively improve the bit error rate performance of the signal detection method.

[0102] like Figure 1 As shown in the figure, when the antenna configuration is 32×128, the modulation mode is 32QAM, and the signal-to-noise ratio is about 13 dB, the bit error rate of the signal detection method proposed in the present invention after three iterations is only 10 -6 , while the signal detection method based on the traditional Barzilai-Borwein algorithm has a bit error rate of up to 10 after 4 iterations. -4 .

[0103] like Figure 2 As shown in the figure, when the antenna configuration is 32×128, the modulation mode is 64QAM, and the signal-to-noise ratio is less than 12 dB, the bit error rate of the signal detection method proposed in the present invention after four iterations is only 10 -6 , which is very close to the bit error rate of the MMSE signal detection method. The bit error rate of the traditional Barzilai-Borwein signal detection method after 4 iterations is as high as 10 -4 .

[0104] like Figure 3 As shown in the figure, when the antenna configuration is 16×64 and the signal-to-noise ratio is 13 dB, the bit error rate of the algorithm proposed in this application after 3 iterations can be only 10 -6 , while the traditional Barzilai-Borwein signal detection method has a bit error rate of up to 10 after 4 iterations. -4 At the same time, it can be seen that the bit error rate of the signal detection method of the present invention is very close to that of the MMSE signal detection method after iterating 4 times.

[0105] In summary, under the same antenna configuration and modulation mode, the signal detection method based on the improved modified Barzilai-Borwein algorithm of the present invention only requires a small number of iterations under different signal-to-noise ratios to approach the bit error rate of the MMSE signal detection method, that is, higher detection accuracy. That is, the present invention improves the signal detection accuracy and optimizes the detection performance while effectively reducing the computational complexity and improving the convergence speed.

[0106] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A signal detection method for a MIMO system, characterized in that: The method comprises: Step 1: Construct a minimum mean square error signal detection algorithm based on the channel gain matrix of the massive MIMO system; Step 2: Convert the problem of solving the minimum mean square error signal detection algorithm into the problem of solving a system of linear equations; Step 3: Convert the problem of solving the linear equations into a problem of iterative solution by a solution algorithm, that is, reconstruct the detection problem into a problem of iterative solution by a solution algorithm; Step 4: Use the improved modified Barzilai-Borwein algorithm to detect the received signal matrix and obtain the estimated value of the transmitted signal The improved modified Barzilai-Borwein algorithm improves the initial value and step size; The initial value of the improved modified Barzilai-Borwein algorithm is: b=H H y Where N represents the number of receiving antennas, K represents the number of transmitting antennas, y represents the received signal vector, and H represents the channel gain matrix; The process of calculating the t+1th iterative signal detection estimation value in step 4 includes: h (t) =On (t) Among them, μ (t) represents a variable step size, θ represents a multiple of the step size, represents the iterative solution obtained at the tth iteration.

2. The signal detection method of the MIMO system according to claim 1, characterized in that: The step size of the improved modified Barzilai-Borwein algorithm is: in, represents the iterative solution obtained by the t-th iteration, and A represents the detection matrix of the minimum mean square error signal detection algorithm.

3. The signal detection method of the MIMO system according to claim 2, characterized in that: The minimum mean square error signal detection algorithm constructed in step 1 is: Where y represents the received signal vector, H represents the channel gain matrix, σ 2 represents the noise variance, K represents the number of transmitting antennas, I K The unit matrix representing the number of transmitting antennas, Represents the estimated value of the transmitted signal.

4. The signal detection method of the MIMO system according to claim 3, characterized in that: The second step includes: The minimum mean square error detection algorithm is converted into an algorithm iterative solution problem using the following formula: Wherein A represents the detection matrix of the minimum mean square error signal detection algorithm; b represents the matched filter output of the received signal.

5. The signal detection method for a MIMO system according to any one of claims 1 to 4, characterized in that: The channel gain matrix H is a Rayleigh fading channel gain matrix.

6. A MIMO signal detection device, comprising: a signal receiving unit, configured to receive a signal; a signal detection unit, configured to detect a received signal using the signal detection method for a MIMO system according to any one of claims 1 to 5; The signal output unit is used to output the detection result of the signal detection unit.

7. A MIMO communication system comprising a transmitter and a receiver, characterized in that: The receiver includes the signal detection device according to claim 6.

8. A MIMO communication method, wherein a signal sent by a transmitting end is transmitted through a channel and received by a receiving end, characterized in that: When receiving a signal, the receiving end detects the received signal using the signal detection method for the MIMO system according to any one of claims 1 to 5.

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