This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

翻译：本文针对基于频分双工的大规模多输入多输出系统，提出了一种基于空间公共稀疏性的自适应信道估计与反馈方案，通过自适应调整训练开销和导频设计，以显著降低的开销可靠估计并反馈下行信道状态信息。具体而言，首先提出一种非正交下行导频设计，该设计与标准正交导频截然不同。通过利用大规模MIMO信道的空间公共稀疏性，提出了一种基于压缩感知的自适应信道状态信息获取方案，其消耗的时隙开销仅自适应取决于信道的稀疏度水平。此外，还提出了一种分布式稀疏度自适应匹配追踪算法，用于联合估计多个子载波的信道。进一步地，利用信道的时间相关性，提供了一种闭环信道跟踪方案，该方案根据先前的信道估计自适应设计非正交导频，以实现增强的信道状态信息获取。最后，我们推广了压缩感知中多测量向量情况的结果，并推导了所提方案的克拉美罗下界，这为我们设计性能更优的非正交导频信号提供了启示。仿真结果表明，所提方案优于对比方案，且能够逼近性能界。