Adaptive sparse channel estimation method for RIS-assisted millimeter wave large-scale MIMO system

Abstract

The invention relates to an adaptive sparse channel estimation method applied to a reconfigurable intelligent surface (RIS)-assisted millimeter wave large-scale MIMO system. According to the method, two progressive algorithms, namely an NLMS-LOG algorithm and an NLMS-NLMF-LOG algorithm, are designed, and the method aims at making full use of the sparsity of millimeter wave channels so as to improve the accuracy and convergence speed of channel estimation. According to the NLMS-LOG algorithm, a logarithm and a penalty term are innovatively introduced into a cost function, so that the convergence speed is effectively increased; the NLMS-NLMF-LOG algorithm fuses the fast convergence advantage of the NLMS algorithm and the excellent performance of the NLMF algorithm under the condition of low signal-to-noise ratio, and the precision of channel estimation is further improved. Simulation experiment results prove that the scheme provided by the invention is obviously superior to the prior art in the aspects of channel estimation precision and convergence speed.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications and is an adaptive sparse channel estimation method for RIS (Reconfigurable Intelligent Surface) assisted millimeter wave massive MIMO system. The method aims to solve the channel estimation problem under channel sparsity and low signal-to-noise ratio (SNR) conditions, and by combining NLMS-LOG and NLMS-NLMF-LOG algorithms, fully utilizes the sparsity and high-order error statistical characteristics of the millimeter wave channel to improve the accuracy and convergence speed of channel estimation. Background Art

[0002] With the development of 5G and future communication systems, millimeter wave communication has become one of the key technologies for the next generation of communication networks due to its wide bandwidth and high-speed data transmission capabilities. However, millimeter wave communication faces serious problems such as signal propagation loss and poor penetration, resulting in unstable communication links. In order to improve the performance of millimeter wave communication, massive MIMO technology has been introduced to enhance the signal transmission quality and improve the reliability of communication by equipping the transmitter and receiver with a large number of antennas.

[0003] In massive MIMO systems, it is crucial to obtain accurate channel state information (CSI). CSI is used to describe the channel characteristics between the transmitter and the receiver, and is the basis for implementing operations such as beamforming, precoding, and power allocation. However, millimeter wave channels usually have significant sparsity, and traditional channel estimation methods fail to fully utilize this characteristic, especially in low signal-to-noise ratio (SNR) environments, where the accuracy and convergence speed of channel estimation are poor. Therefore, it is of great significance to design an adaptive channel estimation method that can fully utilize channel sparsity.

[0004] The present invention proposes an adaptive sparse channel estimation method based on NLMS-LOG and NLMS-NLMF-LOG algorithms. The NLMS-LOG algorithm can effectively accelerate the convergence of sparse channels and improve the accuracy of channel estimation by introducing a logarithmic penalty term in the cost function. The NLMS-NLMF-LOG algorithm further combines the mean square error and the fourth-order error to improve the channel estimation performance under low SNR conditions. These two algorithms can significantly improve the effect of sparse channel estimation and solve the shortcomings of traditional channel estimation methods in millimeter wave large-scale MIMO systems. By adaptively adjusting the algorithm parameters, the system can maintain good channel estimation performance under different signal-to-noise ratio conditions, thereby improving the overall transmission efficiency of the millimeter wave communication system. Summary of the invention

[0005] The purpose of the present invention is to provide an adaptive sparse channel estimation method suitable for RIS-assisted millimeter wave massive MIMO system, which overcomes the estimation difficulties under channel sparsity and low SNR by adaptively adjusting parameters using NLMS-LOG and NLMS-NLMF-LOG algorithms.

[0006] The method mainly includes the following steps:

[0007] 1. Design of adaptive filtering algorithm based on the sparsity of millimeter wave channels

[0008] In the RIS-assisted system, the base station (BS) receives the signal from the user equipment and uses the NLMS-LOG algorithm to adaptively process the channel state information (CSI). The algorithm introduces logarithmic and sparse penalty terms in the cost function to attract small channel coefficients to converge to zero, thereby accelerating the convergence speed. The specific cost function is as follows:

[0009]

[0010] Among them, γ is the penalty coefficient, ω and τ is a positive parameter used to adjust the penalty term, h i (n) represents the channel coefficient.

[0011] 2. High-order error statistics of the NLMS-NLMF-LOG algorithm

[0012] To further improve the accuracy of channel estimation in low SNR environments, the NLMS-NLMF-LOG algorithm combines the mean square error and the fourth-order error. The cost function of the NLMS-NLMF-LOG algorithm consists of the mean square error term and the fourth-order error term, and introduces a sparse penalty term, which is in the form of:

[0013]

[0014] Among them, α is used to balance the mean square error and the fourth-order error, γ, ω, τ is a parameter that controls the penalty term. Through this cost function, NLMS-NLMF-LOG can accelerate the convergence of small channel coefficients in a low SNR environment and improve the estimation accuracy.

[0015] 3. Algorithm optimization and implementation

[0016] According to the update rules of adaptive filtering, the update formulas of NLMS-LOG and NLMS-NLMF-LOG algorithms are:

[0017] NLMS-LOG update formula:

[0018]

[0019] NLMS-NLMF-LOG update formula:

[0020]

[0021] Among them, μ 1 , μ 2 is the update step size, ∈ 2 and ∈ 3 is a positive parameter to prevent the denominator from being zero, and ρ is the penalty coefficient for the zero attraction term.

[0022] 4. Simulation experiments and performance optimization

[0023] For different SNR environments, the algorithm's step size parameters and sparse penalty coefficients are optimized to achieve the best channel estimation performance. Through simulation, it can be concluded that the NLMS-LOG and NLMS-NLMF-LOG algorithms significantly improve the channel estimation accuracy under low SNR conditions and outperform traditional methods in all SNR ranges. Figures 1 to 4 The results of simulation experiments are presented, verifying the superiority of the proposed method under different conditions.

[0024] The present invention aims at the channel estimation challenges under channel sparsity and low signal-to-noise ratio (SNR) conditions in millimeter-wave massive MIMO systems, and proposes an innovative adaptive sparse channel estimation method based on the NLMS-LOG and NLMS-NLMF-LOG algorithms. The core advantage of the present invention is that while ensuring high estimation accuracy, the convergence of sparse channels is accelerated by introducing logarithmic and sparse penalty terms, and the channel estimation performance in low SNR environments is improved by combining high-order error statistics. The NLMS-LOG algorithm makes full use of the sparse characteristics of the channel and effectively reduces the channel estimation error by adaptively adjusting the channel estimation process. The NLMS-NLMF-LOG algorithm incorporates the fourth-order error on the basis of the traditional mean square error, further improving the convergence speed and accuracy of channel estimation. The algorithm design of the present invention not only improves the channel estimation performance of the millimeter-wave MIMO system, but also effectively solves the estimation problem in a low SNR environment, providing a solid technical support for the stable communication of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a model diagram of the RIS-assisted millimeter-wave MIMO system.

[0026] Figure 2 It is a workflow diagram based on the adaptive filtering algorithm.

[0027] Figure 3 : It is the convergence curve diagram of NLMS-NLMF-LOG algorithm.

[0028] Figure 4It is a performance comparison chart of the proposed algorithm and the traditional algorithm under different SNR conditions. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the purpose, technical solutions and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings.

[0030] See also Figure 1 , the application scenario of the present invention is: RIS-assisted millimeter wave MIMO system model. In this system, the base station (BS) is equipped with multiple antennas, and the reflection effect of RIS is used to enhance the communication performance between the BS and the user equipment. The propagation characteristics of the millimeter wave channel show obvious sparsity, and usually only a few significant multipath components participate in signal transmission. The present invention makes full use of the sparsity characteristics of the channel through the designed adaptive channel estimation method, significantly improves the accuracy and convergence speed of channel estimation, especially in a low signal-to-noise ratio (SNR) environment, the performance is particularly outstanding.

[0031] See also Figure 2 The present invention proposes a channel estimation algorithm framework based on an adaptive filter. The adaptive filter processes the input signal in real time and outputs the channel estimation value of the current iteration. The key to this process is to adaptively update the filter weights according to the error signal. The error signal is obtained by comparing the difference between the output signal and the expected signal, and is used to update the weight vector to minimize the cost function. The cost function is defined as a function of the error signal, usually in the form of mean square error. The adaptive algorithm designed by the present invention combines the sparsity constraint to ensure that the sparsity of the channel estimation is fully utilized, thereby accelerating the convergence process.

[0032] See also Figure 3 The convergence curve of the NLMS-NLMF-LOG algorithm of the present invention shows the convergence speed of the algorithm under different iteration numbers. The experimental results show that compared with the traditional channel estimation algorithm, the NLMS-NLMF-LOG algorithm exhibits a faster convergence speed when processing sparse channels.

[0033] See also Figure 4 , showing the performance comparison between the algorithm proposed in the present invention and the traditional algorithm under different SNR conditions. In low, medium and high SNR environments, the NLMS-NLMF-LOG algorithm of the present invention outperforms the traditional algorithm in terms of channel estimation accuracy. The simulation results confirm that the method proposed in the present invention can effectively improve system performance under different SNR conditions.

[0034] The above description is only a preferred example 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. The present invention relates to an adaptive sparse channel estimation method for RIS-assisted millimeter wave massive MIMO system, aiming to solve the channel estimation problem under the condition of channel sparsity and low signal-to-noise ratio (SNR). The method utilizes the sparsity and high-order error statistics of millimeter wave channels by integrating NLMS-LOG and NLMS-NLMF-LOG algorithms to improve the accuracy and convergence speed of channel estimation. (1) Design of adaptive filtering algorithm based on the sparsity of millimeter-wave channels: In the RIS-assisted system, the base station (BS) receives the signal from the user equipment and uses the NLMS-LOG algorithm to adaptively process the channel state information (CSI). By making full use of the sparse characteristics of the millimeter-wave channel, efficient sparse channel estimation is achieved. (2) High-order error statistics adaptive algorithm: To improve the channel estimation performance in low SNR environments, the NLMS-NLMF-LOG algorithm combines the second-order mean square error and the fourth-order error. By introducing a zero attraction penalty term, the channel coefficients are accelerated to converge to zero, thereby improving the estimation accuracy of the algorithm.

2. The method according to claim 1, wherein the adaptive filtering structure comprises the following steps: (21) The NLMS-LOG algorithm is used to perform preliminary optimization of the channel estimation. This algorithm effectively attracts small channel coefficients to converge to zero by introducing logarithmic and penalty terms in the cost function, thereby significantly improving the convergence speed of the channel estimation. (22) Based on the sparse channel characteristics, the NLMS-LOG algorithm updates the filter weights in each iteration and reduces the estimation error by exerting a stronger attraction on the channel coefficients that are smaller than the threshold.

3. The method according to claim 1, wherein the high-order error statistics algorithm further comprises the following steps: (31) In order to improve the estimation performance under low SNR, the NLMS-NLMF-LOG algorithm combines the mean square error and the fourth-order error in the cost function and accelerates the convergence of the channel coefficients by introducing a logarithmic penalty term, thereby improving the accuracy of channel estimation. (32) The algorithm enhances the convergence effect on small channel coefficients in each iteration through the zero attraction mechanism, effectively reducing the estimation error and improving the convergence speed.

4. The method according to claim 1 further comprises the step of optimizing algorithm parameters based on simulation experiments: (41) The step size parameters and sparse penalty coefficients of the NLMS-LOG and NLMS-NLMF-LOG algorithms are optimized for different SNR environments to achieve better channel estimation performance in both high SNR and low SNR environments. (42) Simulation results show that this method has strong channel estimation capabilities in low SNR environments and has significant performance improvement over traditional algorithms over the entire SNR range.

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