Joint Channel Training and Feedback for FDD Massive MIMO Systems

Abstract

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we firstly exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

References in corpus (3)

• Structured Compressed Sensing: From Theory to Applications
• Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
• Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

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• Polar-Cap Codebook Design for MISO Rician Fading Channels with Limited Feedback
• Security Vulnerability of FDD Massive MIMO Systems in Downlink Training Phase
• CS-Based CSIT Estimation for Downlink Pilot Decontamination in Multi-Cell FDD Massive MIMO
• Nonconvex Regularized Gradient Projection Sparse Reconstruction for Massive MIMO Channel Estimation