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

2025-06-17

AI Summary

This paper addresses joint user association and power control optimization under finite-capacity fronthaul links in generalized cell-free massive MIMO systems supporting multi-antenna users and access points, aiming to maximize the sum spectral efficiency (SE) with long-term channel state information (CSI). Method: We propose a unified, degeneratable framework—the first to enable co-design of association and power control under fronthaul constraints—and develop a low-overhead algorithm based on the minorization–maximization (MM) principle. The algorithm integrates user-centric clustering, generalized zero-forcing precoding, and MMSE-SIC reception. Contribution/Results: Numerical evaluations demonstrate substantial SE gains: +59% under centralized beamforming and +312% under distributed beamforming, significantly outperforming heuristic baselines. Furthermore, we systematically characterize fundamental trade-offs between cooperative cluster size and key system parameters—such as fronthaul capacity, antenna density, and user distribution—providing critical design insights for practical deployment.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Optimize user association and power control for sum spectral efficiency
• Address fronthaul constraints in cell-free massive MIMO systems
• Analyze trade-offs in cooperative beamforming designs

Innovation

• Fronthaul-limited CF-mMIMO with cooperative beamforming
• MMSE-SIC scheme for signal detection
• MM technique for sum SE optimization