Sequential Processing Strategies in Fronthaul Constrained Cell-Free Massive MIMO Networks

TL;DR

This paper proposes two sequential processing strategies for cell-free massive MIMO networks with daisy-chain fronthaul, aiming to mitigate fronthaul compression effects and improve spectral efficiency.

Contribution

It introduces two novel sequential processing strategies that enhance spectral efficiency in fronthaul constrained CFmMIMO networks.

Findings

Two proposed strategies outperform equal capacity allocation.

Two-Path users' signal estimation improves sum SE.

Linear fronthaul capacity increase benefits network performance.

Abstract

In a cell-free massive MIMO (CFmMIMO) network with a daisy-chain fronthaul, the amount of information that each access point (AP) needs to communicate with the next AP in the chain is determined by the location of the AP in the sequential fronthaul. Therefore, we propose two sequential processing strategies to combat the adverse effect of fronthaul compression on the sum of users' spectral efficiency (SE): 1) linearly increasing fronthaul capacity allocation among APs and 2) Two-Path users' signal estimation. The two strategies show superior performance in terms of sum SE compared to the equal fronthaul capacity allocation and Single-Path sequential signal estimation.