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Efficient channel state information at transmitter (CSIT) for frequencydivision duplex (FDD) massive MIMO can facilitate its backward compatibilitywith existing FDD cellular networks. To date, several CSIT estimation schemeshave been proposed for FDD single-cell massive MIMO systems, but they fail toconsider inter-cell-interference (ICI) and suffer from downlink pilotcontamination in multi-cell scenario. To solve this problem, this paperproposes a compressive sensing (CS)-based CSIT estimation scheme to combat ICIin FDD multi-cell massive MIMO systems. Specifically, angle-domain massive MIMOchannels exhibit the common sparsity over different subcarriers, and suchsparsity is partially shared by adjacent users. By exploiting these sparsityproperties, we design the pilot signal and the associated channel estimationalgorithm under the framework of CS theory, where the channels associated withmultiple adjacent BSs can be reliably estimated with low training overhead fordownlink pilot decontamination. Simulation results verify the good downlinkpilot decontamination performance of the proposed solution compared to itsconventional counterparts in multi-cell FDD massive MIMO.

CS-Based CSIT Estimation for Downlink Pilot Decontamination in Multi-Cell FDD Massive MIMO