Abstract: Aiming at the problem that pilot overhead required by downlink channel estimation of FDD (Frequency-Division Duplexing) massive MIMO (Multiple-Input Multiple-Output) was unaffordable, a pseudo-random pilot optimization scheme based on Compressive Sensing (CS) techniques with non-orthogonal pilot at the base station and the objective to minimize the cross correlation of the measurement matrix was proposed firstly. Then, a crossover and mutation judgment mechanism and an inner loop and outer loop mechanism were introduced to ensure the optimization of pilot sequence. Secondly, a Channel State Information (CSI) estimation algorithm based on CS techniques by utilizing the spatially common sparsity and temporal correlation in wireless MIMO channels was presented. Matrix estimation is performed by using LMMSE (Linear Minimum Mean Square Error) algorithm to accurately obtain CSI. Analysis and simulation results show that compared with random search pilot optimization scheme, location-based optimization scheme, local common support algorithm, Adaptive Structured Subspace Pursuit (ASSP) algorithm, Orthogonal Matching Pursuit (OMP) algorithm and Stepwise Orthogonal Matching Pursuit (StOMP) algorithm, the proposed algorithm can significantly achieve good channel estimation performance in the case of low pilot overhead ratio and low Signal-to-Noise Ratio (SNR).

Key words: massive Multiple-Input Multiple-Output (MIMO), channel estimation, pilot optimization, Compressive Sensing (CS), spatial and temporal correlation

摘要： 针对频分双工（FDD）大规模MIMO系统下行信道估计过程中由于导频数和基站天线数成正比会造成巨大的导频开销这一问题，首先提出一种基于压缩感知（CS）技术的伪随机导频优化方案，该方案令基站发射非正交导频信号，并且以最小化观测矩阵的互相关为优化目标，通过引入交叉、变异判断机制和内、外循环机制以实现对导频序列的优化；其次，联合利用无线MIMO信道的空间公共稀疏性和时间相关性提出一种基于压缩感知技术的信道状态信息（CSI）估计算法，利用线性最小均方误差（LMMSE）算法进行矩阵估计以精确获取CSI。分析和仿真结果表明，与随机搜索算法、逐位置优化方案、局部公共支撑算法、自适应结构子追踪（ASSP）算法、正交匹配追踪（OMP）算法以及逐步正交匹配追踪（StOMP）算法相比，所提算法在低导频开销比和低信噪比（SNR）的情况下均可以维持良好的信道估计性能。

关键词: 大规模MIMO, 信道估计, 导频优化, 压缩感知, 空时相关性