FDD Massive MIMO Channel Estimation in Dynamic Scenarios Based on Class-Specific Dictionary Learning

Abstract

This letter addresses the frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink (DL) channel estimation problem in dynamic scenarios. We propose a multipath angle of arrival (AoA) and angle of departure (AoD)-based class-specific dictionary learning (MACDL) algorithm, where the discriminative information of multipath AoA/AoD is exploited via supervised dictionary learning to enhance the model’s generalization ability, which enables it working well in dynamic environments without constant retraining. Simulation results show that the proposed algorithm significantly reduces the pilot overhead and achieves better stability than other channel estimation schemes.