| 摘要: |
| 针对超大规模多输入多输出(Extremely Large-scale Multiple-Input Multiple-Output，XL-MIMO)系统近场的极域信道特性，同时考虑到未来信道的低信噪比环境以及匹配追踪算法中的先验稀疏度信息难以获取的问题，提出了一种基于最大似然原理的子载波加权选择的分段弱正交匹配追踪算法(Subcarrier Weight Selection Stagewise Weak Orthogonal Matching Pursuit，SWS-SWOMP)。SWS-SWOMP算法利用公共稀疏性对子载波加权并在获取支持度后进行再选择，从而在低信噪比环境下以及稀疏度未知的情况下提高最终支持度的可靠性。仿真结果表明，所提XL-MIMO近场信道估计算法在低信噪比下归一化均方误差性能相对于极域同步正交匹配追踪(Polar-domain Simultaneous Orthogonal Matching Pursuit，P-SOMP)算法平均提高约3 dB，更符合实际信道场景。 |
| 关键词: 超大规模MIMO(XL-MIMO) 近场信道估计 极域 压缩感知 最大似然 |
| DOI：10.20079/j.issn.1001-893x.240117009 |
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| 基金项目:重庆市自然科学基金创新发展联合基金（中国星网）(CSTB2023NSCQ-LZX0114) |
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| A Near-field Channel Estimation Method for Extremely Large-scale MIMO System Based on Maximum Likelihood |
| WANG Huahua,ZHENG Shaojie,WANG Lei |
| (School of Communications and Information Engineering，Chongqing University of Posts and Telecommunications，Chongqing 400065,China) |
| Abstract: |
| In order to solve the polar channel characteristics of the extremely large-scale MIMO(XL-MIMO) system in the near field,and considering the low signal-to-noise ratio(SNR) environment of future channel and the difficulty of obtaining a priori sparsity information in the match pursuit(MP) algorithm,a subcarrier weight selection stagewise weak orthogonal matching pursuit(SWS-SWOMP) algorithm based on the principle of maximum likelihood(ML) is proposed.The SWS-SWOMP algorithm uses common sparsity to weight the subcarriers and select them after obtaining the support,so as to improve the reliability of the final support in the environment with low SNR and unknown sparsity.The simulation results show that the normalized mean square error performance of the proposed XL-MIMO near-field channel estimation algorithm is about 3 dB higher than that of the polar-domain simultaneous orthogonal matching pursuit(P-SOMP) algorithm under the low SNR,which is more in line with the actual channel scenario. |
| Key words: extremely large-scale MIMO(XL-MIMO) near-field channel estimation polar domain compressed sensing maximum likelihood |
