Cluster-wise processing in fronthaul-aware cell-free massive MIMO systems

2025-10-18

AI Summary

For user-centric cell-free massive MIMO systems under fronthaul capacity constraints, this paper proposes a scalable processing cluster (PC)-based network architecture to jointly optimize user association, precoding, and power allocation using only local channel state information (CSI). The core contribution is a cluster-level weighted sum pseudo-spectral efficiency (pseudo-SE) maximization framework that explicitly distinguishes intra-cluster interference from inter-cluster signal leakage while rigorously enforcing fronthaul bandwidth constraints. To solve the resulting non-convex mixed-integer optimization problem, we design an enhanced distributed weighted minimum mean square error (WMMSE) algorithm, supporting multi-timescale coordination and adjustable computational complexity. Simulation results demonstrate that the proposed method significantly improves system spectral efficiency under fronthaul constraints, closely approaching the true achievable rates, while ensuring scalability and practical deployability.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We exploit a general cluster-based network architecture for a fronthaul-limited user-centric cell-free massive multiple-input multiple-output (CF-mMIMO) system under different degrees of cooperation among the access points (APs) to achieve scalable implementation. In particular, we consider a CF-mMIMO system wherein the available APs are grouped into multiple processing clusters (PCs) to share channel state information (CSI), ensuring that they have knowledge of the CSI for all users assigned to the given cluster for the purposes of designing resource allocation and precoding. We utilize the sum pseudo-SE metric, which accounts for intra-cluster interference and intercluster-leakage, providing a close approximation to the true sum achievable SE. For a given PC, we formulate two optimization problems to maximize the cluster-wise weighted sum pseudo-SE under fronthaul constraints, relying solely on local CSI. These optimization problems are associated with different computational complexity requirements. The first optimization problem jointly designs precoding, user association, and power allocation, and is performed at the small-scale fading time scale. The second optimization problem optimizes user association and power allocation at the large-scale fading time scale. Accordingly, we develop a novel application of modified weighted minimum mean square error (WMMSE)-based approach to solve the challenging formulated non-convex mixed-integer problems.

Problem

• Scalable implementation of cluster-based cell-free massive MIMO systems
• Optimizing resource allocation under fronthaul capacity constraints
• Maximizing spectral efficiency while managing inter-cluster interference

Innovation

• Cluster-based network architecture for scalable cell-free MIMO
• Sum pseudo-SE metric accounting for interference and leakage
• Modified WMMSE approach solving non-convex optimization problems