宽带大规模MIMO-OFDM系统自适应稀疏信道估计方案  

Adaptive sparse channel estimation for broadband massive MIMO-OFDM systems

作　　者：黄灿 李素月[1] 王安红[1] Huang Can;Li Suyue;Wang Anhong(College of Electronic Information Engineering,Taiyuan University of Science&Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学电子信息工程学院

出　　处：《计算机应用研究》2019年第11期3438-3443,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61501315,61672373);山西省高等学校科技创新项目(2015169);山西省科技创新团队资助项目(201705D131025);校博士启动金资助项目(20142005);山西省“1331工程”重点创新团队资助项目(KITSX1331)

摘　　要：大规模MIMO-OFDM系统下行链路利用压缩感知算法获得信道状态信息需要已知信号的稀疏度作为先验条件,然而实际环境中,无线信道的稀疏度是未知的。利用大规模MIMO信道的空时共同稀疏性的特点与不同SNR下设置不同停止迭代阈值的思想改进压缩感知重构算法,目的在于使所提算法不仅提升估计性能,还可以准确获得信道的动态稀疏度。通过实验可知,相比传统的CoSaMP算法和S-CoSaMP算法,SSA-CoSaMP算法在同等信噪比下具有更良好的信道估计性能,并且可以自适应地获取稀疏度,更适合实际工程中应用。In massive MIMO-OFDM system,most of the compressed sensing theory reconstruction algorithms need the sparsity of the known signal as a priori condition.However,in the actual environment,the sparsity of the wireless channel is unknown.Therefore,the proposed algorithm used the space-time common sparsity of the Massive MIMO channel to reasonably set the stopping iteration parameters under different SNR to obtain accurate dynamic sparsity.The experimental results show that,compared with the traditional CoSaMP and S-CoSaMP algorithms,the SSA-CoSaMP algorithm has better channel estimation performance under the same SNR and can obtain the sparsity adaptively.

关 键 词：大规模MIMO 压缩感知 稀疏度自适应 稀疏信道估计 空时共同稀疏性 

分 类 号：TP929.5[自动化与计算机技术]