Efficient Design of Fronthaul-Constrained Uplink Reception for Cell-Free XL-MIMO

2026-04-22

AI Summary

This work addresses the uplink receiver design under limited fronthaul capacity in cell-free massive MIMO systems by proposing a joint optimization framework that balances scalability and fronthaul efficiency. The approach co-designs linear transformation matrices at access points and fronthaul compression strategies to maximize the uplink sum rate under finite fronthaul constraints. Leveraging the fractional programming (FP) framework, we develop a low-complexity Accelerated Fractional Programming (A-FP) algorithm along with a decentralized implementation that decouples fronthaul overhead from the number of antennas. Compared to generic solvers, A-FP substantially reduces computational complexity, while significantly outperforming scalable baseline schemes that rely solely on local channel state information.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links. We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sumrate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.

Problem

• Cell-Free XL-MIMO
• Fronthaul-Constrained
• Uplink Reception
• Sum-Rate Maximization
• Scalable Design

Innovation

• Cell-Free XL-MIMO
• Fronthaul Compression
• Fractional Programming
• Accelerated FP
• Decentralized Implementation