大规模MIMO系统中基于空时相关性的导频减小算法

在大规模多输入多输出(multiple input multiple output,MIMO)系统信道估计过程中,基站向用户端发送导频信号.由于导频数量与基站发射天线的数量成正比,传统信道估计过程会产生巨大的导频开销,尤其是对于采用频分双工通信方式的(frequency-division duplexing,FDD)大规模MIMO系统.为了解决这一问题,通过利用无线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(compressed sensing,CS)技术的导频开销减小算法,其中,空时相关性用来提高信道估计精度.该算法能够在未知大规模MIMO系统信道稀疏度的情况下,自适应地获取精确的信道状态信息.分析和仿真结果表明提出的算法在减少导频开销方面优于局部公共支撑算法,同时能够维持良好的信道估计性能.

Pilot reduction using spatial and temporal correlation in massive MIMO systems

In the massive multiple input multiple output (MIMO) systems, the base station sends pilot signals for channel estimation at users. Since the number of pilots is proportional to the number of transmit antennas, pilot overhead required by conventional channel estimation can be prohibitively large, especially for frequency-division duplexing (FDD) massive MIMO. To solve this problem, we present a pilot overhead reduction algorithm based on compressive sensing (CS) techniques by utilizing the spatially common sparsity and temporal correlation in wireless MIMO channels, whereby the spatial and temporal correlation is exploited to improve the channel estimation accuracy. The proposed algorithm can adaptively acquire the accurate channel state information without the knowledge of the sparsity level of massive MIMO channel. Analysis and simulation results show that the proposed algorithm outperforms Locally Common Support Algorithm in pilot overhead reduction, and it is capable of achieving good channel estimation performance.