A Framework to Channel Feedback and Reconstruction using Matrix Completion in Massive MIMO Systems

Abstract

In this paper, we are interested in the problem of channel feedback and reconstruction in frequency division duplexing (FDD)-based massive multiple-input-multiple-output (MIMO) systems. We propose a general framework that allows reducing the overhead significantly in the uplink feedback control channel when assuming massive antenna arrays at both ends of the wireless link. We investigate the methods capitalize on matrix completion to reducing the feedback overhead. The fundamental idea of the proposed method is to explore the low-rank structure of the channel for its accurate reconstruction at the transmitter
side (Tx) with very few uplink feedback information, using matrix completion technique. The proposed framework consisting of two stages. First, upon reception of downlink pilots, the receiver side (Rx) undersamples either the received pilot data matrix or the estimated channel matrix and feeds back only a fraction of their entries to the Tx, throwing away the remaining ones. Then, under the assumption of reduced scattering propagation, the Tx capitalizes on matrix completion to recover either the downlink pilots or directly reconstruct the downlink channel. We consider two application examples: i) backhauling communication and ii) a multi-user equipment (UE) scenario with perfect and
imperfect instantaneous channel knowledge. Due to data/channel undersampling, energy consumption at a receiver can be reduced during the uplink feedback transmission. Simulation results show that, compared to the conventional full-feedback approach, which requires feedback of the entire matrix, the proposed solution can decrease the feedback overload in more than 90% for low-rank channels, while providing good channel estimation accuracy, bit error rate (BER) under maximum ratio transmission (MRT) precoding and goodput.