Master-Assisted Channel Estimation for Cell-Free Massive MIMO Networks

TL;DR

This paper introduces MACE, a novel channel estimation method for cell-free massive MIMO networks that leverages inter-AP signal correlation through partial centralization, improving estimation accuracy while reducing complexity.

Contribution

The paper proposes a master-assisted channel estimation scheme that exploits inter-AP signal correlation via partial centralization, enhancing performance over local estimation methods.

Findings

MACE outperforms local channel estimation in numerical experiments.

The scheme balances centralized and local processing to reduce fronthaul and computational costs.

Leveraging inter-AP correlation improves channel estimation accuracy.

Abstract

Cell-free massive-multiple-input-multiple-output (CFmMIMO) is a key enabler for sixth-generation (6G) wireless communication networks, where distributed access points (APs) jointly serve user equipments (UEs). In commonly adopted channel models for CFmMIMO networks, inter-AP channel correlation is assumed to be absent, thereby eliminating the potential benefits of centralized processing. However, by carefully designing the pilot transmission phase, the AP received signals during pilot transmission can become correlated, and thus, centralization can improve channel estimation performance, despite the absence of inter-AP channel correlation. In this paper, we propose a channel estimation scheme, termed master-assisted channel estimation (MACE), that aims to leverage inter-AP signal correlation by means of partially centralized processing and hence improve channel estimation performance. In MACE, a subset of APs fuse and forward their received pilot signals to a master AP, which then performs channel estimation using the fused signals together with its locally received signals. This scheme strikes a balance between local and fully centralized processing by leveraging inter-AP signal correlation, while reducing fronthaul signaling and computational complexity. Numerical experiments demonstrate that MACE consistently outperforms local channel estimation, where inter-AP signal correlation is neglected.