基于贝叶斯压缩感知的毫米波MIMO信道估计  

Millimeter Wave MIMO Channel Estimation Based on Bayesian Compressive Sensing

作　　者：彭一文 任文平[1] 钱蓉蓉 PENG Yi-wen;REN Wen-ping;QIAN Rong-rong(School of Information Science and Engineering,Yunnan University,Kunming Yunnan 650500,China)

机构地区：[1]云南大学信息学院,云南昆明650500

出　　处：《计算机仿真》2020年第4期151-154,334,共5页Computer Simulation

基　　金：国家自然科学基金项目(61701433);云南省科技厅面向项目(2018FB099)。

摘　　要：针对毫米波大规模多输入多输出(MIMO)通信系统中存在的硬件成本高、能耗大等问题,混合模拟-数字的收发机架构是一个很有前景的解决方案,然而系统的信道估计问题却成为一个挑战。在考虑正交频分复用和频率选择性衰落信道模型的前提下,提出了一种使用贝叶斯压缩感知理论来估计信道的方法。贝叶斯压缩感知算法可以在稀疏信道先验知识不完备的情况下,实现更高精度的信道估计。仿真结果验证了所提方法的有效性,与正交匹配追踪算法相比,在信噪比为30dB时,归一化均方误差降低了约25dB。Hybrid analog-digital transceiver architecture is a promising solution for the problems of high cost and power consumption of hardware in millimeter-wave massive multiple-input multiple-output(MIMO) communication systems. However, the channel estimation problem of the system has become a challenge. Considering the orthogonal frequency division multiplexing(OFDM) and frequency selective fading channel model, a channel estimation method is proposed, which uses the Bayesian compressed sensing(BCS) theory. The proposed algorithm based on BCS can achieve more accurate channel estimation with an incomplete sparse channel prior knowledge. The simulation results verify the effectiveness of the proposed method. Compared with the orthogonal matching pursuit algorithm, the normalized mean square error(NMSE) is reduced by about 25 dB when the SNR is 30 dB.

关 键 词：毫米波 大规模多输入多输出 正交频分复用 贝叶斯压缩感知 

分 类 号：TN928[电子电信—通信与信息系统]