Fronthaul-Aware User-Centric Generalized Cell-Free Massive MIMO Systems

TL;DR

This paper proposes a fronthaul-aware, user-centric generalized cell-free massive MIMO system framework that optimizes user association and power control to maximize spectral efficiency under practical constraints.

Contribution

It introduces a comprehensive optimization framework with a novel algorithm for user association and power control in generalized CF-mMIMO systems considering fronthaul limitations.

Findings

Power optimization improves sum SE by up to 59% in centralized CB.

Distributed beamforming sees a 312% SE improvement with the proposed method.

Trade-offs between different cluster sizes are analyzed for system design.

Abstract

We consider fronthaul-limited generalized zeroforcing-based cell-free massive multiple-input multiple-output (CF-mMIMO) systems with multiple-antenna users and multipleantenna access points (APs) relying on both cooperative beamforming (CB) and user-centric (UC) clustering. The proposed framework is very general and can be degenerated into different special cases, such as pure CB/pure UC clustering, or fully centralized CB/fully distributed beamforming. We comprehensively analyze the spectral efficiency (SE) performance of the system wherein the users use the minimum mean-squared errorbased successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. Specifically, we formulate an optimization problem for the user association and power control for maximizing the sum SE. The formulated problem is under per-AP transmit power and fronthaul constraints, and is based on only long-term channel state information (CSI). The challenging formulated problem is transformed into tractable form and a novel algorithm is proposed to solve it using minorization maximization (MM) technique. We analyze the trade-offs provided by the CF-mMIMO system with different number of CB clusters, hence highlighting the importance of the appropriate choice of CB design for different system setups. Numerical results show that for the centralized CB, the proposed power optimization provides nearly 59% improvement in the average sum SE over the heuristic approach, and 312% improvement, when the distributed beamforming is employed.