基于压缩感知的MIMO-OFDM信道估计中的导频设计方案

多输入多输出-正交频分复用(Multiple input multiple output-orthogonal frequency division multiplexing，MIMO-OFDM)系统作为MIMO系统和OFDM系统的结合，具有很高的频带利用率并能有效地对抗无线信道的多径效应。本文研究了MIMO-OFDM系统稀疏信道估计及其导频优化，将信道估计问题转化为压缩感知(Compressed sensing，CS)理论中的稀疏信号重建问题，将最小化测量矩阵的互相关作为导频优化的目标。结合已有的随机序贯搜索(Stochastic sequential search，SSS)和扩展算法2(Extension scheme 2，ES2)算法以及导频移位机制，提出了一种快速的导频优化算法随机搜索移位算法(Stochastic sequential search-shift mechanism，SSS-SM)。此算法的运算复杂度远低于已有的ES2算法，运算时间不受发射天线数影响。将SSS-SM算法和ES2算法分别获得的导频设计结果应用于MIMO-OFDM系统的信道估计，仿真结果表明，采用SSS-SM算法可以更低的算法复杂度获得与ES2算法相同的信道估计性能；高信噪比情况下，SSS-SM算法对应的均方误差(Mean square error，MSE)比ES2平均低约3~5 dB，因此这种方法在高信噪比下更有优势。

Pilot Design Schemes for Compressed Sensing-Based MIMO-OFDM Channel Estimation

As a combination of MIMO and OFDM systems, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system has high band utilization and can effectively combat multipath effects in wireless channels. In the paper, we studies sparse channel estimation and pilot optimization problems for MIMO-OFDM systems. The channel estimation problem in MIMO-OFDM systems is transformed into the sparse signal reconstruction problem in compressed sensing (CS) theory. The pilot optimization is based on minimizing the mutual coherence of the measurement matrix. In combination with existing stochastic sequential search (SSS) and extension scheme 2(ES2) algorithms and pilot shift mechanism, a fast pilot optimization algorithm stochastic sequential search-shift mechanism (SSS-SM) is proposed. The algorithm has lower computational complexity, and the computation time is not affected by the number of transmit antennas. The pilot design results obtained by SSS-SM algorithm and ES2 algorithm are applied to the channel estimation of MIMO-OFDM system. Simulation results show that SSS-SM can achieve the same channel estimation performance as ES2 with less computational complexity. In the case of high signal-to-noise ratio (SNR), the mean square error (MSE) of SSS-SM is about averaged 3 dB lower than that of ES2, which shows that the method has advantages over high SNR.