Efficient channel state information at transmitter (CSIT) for frequency division duplex (FDD) massive MIMO can facilitate its backward compatibility with existing FDD cellular networks. To date, several CSIT estimation schemes have been proposed for FDD single-cell massive MIMO systems, but they fail to consider inter-cell-interference (ICI) and suffer from downlink pilot contamination in multi-cell scenario. To solve this problem, this paper proposes a compressive sensing (CS)-based CSIT estimation scheme to combat ICI in FDD multi-cell massive MIMO systems. Specifically, angle-domain massive MIMO channels exhibit the common sparsity over different subcarriers, and such sparsity is partially shared by adjacent users. By exploiting these sparsity properties, we design the pilot signal and the associated channel estimation algorithm under the framework of CS theory, where the channels associated with multiple adjacent BSs can be reliably estimated with low training overhead for downlink pilot decontamination. Simulation results verify the good downlink pilot decontamination performance of the proposed solution compared to its conventional counterparts in multi-cell FDD massive MIMO.

翻译：为解决这一问题,本文件建议采用基于压缩的遥感(CS)基础CSIT估算计划,在捍卫民主阵线的多细胞大型MIMO系统中打击ICI。具体地说,角多面大型MIMO频道展示了对不同子载体的共同渗透性,而相邻用户也部分分享了这种弥散性。我们利用这些宽度特性,在CS理论的框架内设计了试点信号和相关的频道估算算法,在CS理论的框架内,可以可靠地估计与多个相邻的BS相关的频道和低培训管理器,以便进行下行点净化试验。模拟结果核查了拟议解决办法与多细胞捍卫民主阵线大型MIMO常规对应方相比的良好下行点试点净化性表现。