Sparse Channel Estimation for Massive MIMO with 1-bit Feedback per Dimension

TL;DR

This paper introduces a practical channel estimation method for massive MIMO systems that uses one-bit feedback per dimension and joint compressed sensing to efficiently recover channel information, improving beamforming performance.

Contribution

It proposes a novel one-bit compressed sensing algorithm exploiting joint sparsity for efficient CSIT estimation in FDD massive MIMO systems.

Findings

Nearly achieves maximum SNR for beamforming

Reduces training and feedback overhead

Accurately recovers channel directions

Abstract

In massive multiple-input multiple-output (MIMO) systems, acquisition of the channel state information at the transmitter side (CSIT) is crucial. In this paper, a practical CSIT estimation scheme is proposed for frequency division duplexing (FDD) massive MIMO systems. Specifically, each received pilot symbol is first quantized to one bit per dimension at the receiver side and then the quantized bits are fed back to the transmitter. A joint one-bit compressed sensing algorithm is implemented at the transmitter to recover the channel matrices. The algorithm leverages the hidden joint sparsity structure in the user channel matrices to minimize the training and feedback overhead, which is considered to be a major challenge for FDD systems. Moreover, the one-bit compressed sensing algorithm accurately recovers the channel directions for beamforming. The one-bit feedback mechanism can be implemented in practical systems using the uplink control channel. Simulation results show that the proposed scheme nearly achieves the maximum output signal-to-noise-ratio for beamforming based on the estimated CSIT.