Compressed Channel Feedback for Correlated Massive MIMO Systems

TL;DR

This paper introduces a novel sparsifying basis and reconstruction algorithm for compressing channel state information in massive MIMO systems, improving feedback efficiency and performance in multi-user scenarios.

Contribution

It proposes a new sparsifying basis reflecting long-term channel characteristics and a new CS reconstruction algorithm, enhancing CSI compression without frequent basis updates.

Findings

Better CSI feedback performance in single-user scenarios

Improved multi-user system performance

Effective dimensionality reduction for compression

Abstract

Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.