大规模MIMO-OFDM系统结构化压缩感知信道估计中导频优化方法研究

对于FDD模式的大规模MIMO-OFDM系统，本文研究了将结构化压缩感知理论用于该系统的稀疏信道估计。考虑在该系统中将每个发送天线上的导频重叠放置，即每个发送天线可以在相同的时频资源块上发送导频符号,那么此时的系统稀疏信道估计问题可以建模为结构化压缩感知重建问题。为了优化导频位置和导频符号来改进稀疏信道估计的质量，提出了一种最小化完全块间相关值的导频优化准则以及基于此准则的导频搜索算法，完全块间相关值是结构化压缩感知框架下衡量恢复矩阵子块间相关程度的量值。仿真结果表明，与其他导频相比，使用此优化方法获得的导频可以使信道估计误差明显减小，其性能增益大约为2~5 dB。另外，仿真结果还表明，在相同的导频数量前提下，使用优化导频获得的性能增益会随着发送天线数量的增加而变得更加明显。 

Optimizing Pilots for Structured Compressive Sensing Based Channel Estimation in Massive MIMO-OFDM systems

This paper addresses the sparse channel estimation problem in FDD large-scale multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems from the perspective of structured compressive sensing. In this system, the pilots in different transmit antennas are superimposed and the problem of sparse channel estimation can be modeled as the reconstruction problem of structured compressive sensing. To improve the performance of structured compressive sensing-based channel estimation, a mutual coherence-related criterion is proposed to optimize pilots and a random research algorithm is provided to obtain optimized pilots. Simulation results show that, as compared with other randomly generated pilots, employing the pilots obtained by proposed optimizing algorithm can reduce the error of channel estimation obviously, and thus improve the channel estimation performance. The performance gain obtained by employing optimizing pilots is about 2~5dB. Simulation results also show that, when the number of pilots is the same, the performance gains by employing optimizing pilots become larger with the increase of the number of transmit antennas.