Hybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization

TL;DR

This paper introduces a scalable CFmMIMO approach that significantly reduces fronthaul signaling and CPU load by combining network- and user-centric clustering, with minimal impact on spectral efficiency.

Contribution

It proposes a novel low-complexity online UE-AP association method that minimizes fronthaul signaling in large-scale CFmMIMO networks, handling dynamic UEs with local information.

Findings

Up to 94% reduction in fronthaul signaling load.

Up to 83% reduction in CPU processing power.

Minimal spectral efficiency loss of 8.6% or none.

Abstract

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (UEs). However, its cooperative nature can result in intense fronthaul signaling between CPUs in large-scale networks. To reduce the inter-CPU fronthaul signaling for systems with limited fronthaul capacity, we propose a low-complexity online UE-AP association approach for scalable CFmMIMO that combines network- and user-centric clustering methodologies, relies on local channel information only, and can handle dynamic UE arrivals. Numerical results demonstrate that compared to the state-of-the-art method on fronthaul signaling minimization, our approach can save up to 94% of the fronthaul signaling load and 83% of the CPU processing power at the cost of only up to 8.6% spectral efficiency loss, or no loss in some cases.