Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO

2025-10-08

AI Summary

To address fronthaul capacity constraints and load imbalance in cell-free massive MIMO systems under dynamic user density variations, this paper proposes a rate-distortion-theoretic framework for dynamic fronthaul quantization rate optimization. The method jointly optimizes user-centric cluster formation, decentralized signal processing, fronthaul routing, and cluster processor placement. A mixed-integer linear programming (MILP) formulation enables coordinated scheduling of fronthaul traffic and computational resources. Compared to static quantization schemes, the proposed approach significantly improves fronthaul load stability—reducing load fluctuations by over 60% under large user count variations—while incurring less than 2% degradation in physical-layer spectral efficiency. This demonstrates an excellent trade-off among scalability, robustness to user density dynamics, and performance preservation.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the fronthaul. We consider a routed fronthaul network, where the cluster processor placement and fronthaul traffic routing are jointly optimized with a mixed-integer linear program. For different numbers of users in the network, we investigate the effect of fronthaul quantization rates, a system parameter computed based on rate-distortion theory. Our results show that with optimized quantization rates, the fronthaul load is quite stable for a wide range of user loads without significant PHY performance loss. This demonstrates that the cell-free massive MIMO PHY and fronthaul network are resilient to varying user densities.

Problem

• Optimizing fronthaul quantization for flexible user loads in cell-free massive MIMO systems
• Jointly optimizing cluster processor placement and fronthaul traffic routing
• Investigating quantization rates' impact on fronthaul load and spectral efficiency

Innovation

• Optimized fronthaul quantization for flexible user load
• Joint cluster placement and traffic routing optimization
• Rate-distortion theory based quantization rate computation