Belief Information based Deep Channel Estimation for Massive MIMO Systems

TL;DR

This paper introduces a belief information mechanism that enhances multi-antenna channel estimation in massive MIMO systems, improving accuracy or reducing pilot overhead, thereby increasing spectral efficiency in next-generation wireless communications.

Contribution

It proposes a plug-and-play belief information module that adapts existing single-antenna estimation networks for multi-antenna systems, exploiting spatial correlation.

Findings

Improves channel estimation by 1-2 dB in performance.

Reduces pilot overhead by one-third to one-half.

Effective in challenging channel conditions.

Abstract

In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. However, current MIMO systems rely on inserting pilot signals to achieve accurate channel estimation. As the increase of transmit stream, the pilots consume a significant portion of transmission resources, severely reducing the spectral efficiency. In this correspondence, we propose a belief information based mechanism. By introducing a plug-and-play belief information module, existing single-antenna channel estimation networks could be seamlessly adapted to multi-antenna channel estimation and fully exploit the spatial correlation among multiple antennas. Experimental results demonstrate that the proposed method can either improve 1 ~ 2 dB channel estimation performance or reduce 1/3 ~ 1/2 pilot overhead, particularly in bad channel conditions.