Joint Channel Training and Feedback for FDD Massive MIMO Systems

TL;DR

This paper introduces a joint channel training and feedback scheme for FDD massive MIMO systems that leverages temporal correlation and structured sparsity to significantly reduce overhead while maintaining accurate channel state information.

Contribution

It proposes a novel differential training and feedback method combined with a structured compressive sampling algorithm for efficient CSIT acquisition in FDD massive MIMO.

Findings

Reduces training and feedback overhead substantially.

Achieves reliable CSIT with structured sparsity exploitation.

Demonstrates effectiveness through simulation results.

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.