Statistics Approximation-Enabled Distributed Beamforming for Cell-Free Massive MIMO

2026-02-03

AI Summary

This work addresses the high backhaul overhead and implementation complexity of centralized MMSE beamforming in cell-free massive MIMO systems, which relies on global instantaneous channel state information. To overcome this limitation, the authors propose the GSLI-MMSE scheme, which innovatively approximates the instantaneous channel contributions from other access points using only global statistical channel information. This enables each access point to perform distributed MMSE beamforming with local instantaneous and global statistical channel knowledge. Leveraging uplink–downlink duality, the precoder is derived using the matrix inversion lemma under Rician and Rayleigh correlated fading models. In Rician fading scenarios with static users and fixed line-of-sight paths, the proposed method achieves performance close to that of centralized MMSE while substantially reducing coordination overhead, demonstrating its suitability for multi-antenna access points and diverse channel environments.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

We study a distributed beamforming approach for cell-free massive multiple-input multiple-output networks, referred to as Global Statistics&Local Instantaneous information-based minimum mean-square error (GSLI-MMSE). The scenario with multi-antenna access points (APs) is considered over three different channel models: correlated Rician fading with fixed or random line-of-sight (LoS) phase-shifts, and correlated Rayleigh fading. With the aid of matrix inversion derivations, we can construct the conventional MMSE combining from the perspective of each AP, where global instantaneous information is involved. Then, for an arbitrary AP, we apply the statistics approximation methodology to approximate instantaneous terms related to other APs by channel statistics to construct the distributed combining scheme at each AP with local instantaneous information and global statistics. With the aid of uplink-downlink duality, we derive the respective GSLI-MMSE precoding schemes. Numerical results showcase that the proposed GSLI-MMSE scheme demonstrates performance comparable to the optimal centralized MMSE scheme, under the stable LoS conditions, e.g., with static users having Rician fading with a fixed LoS path.

Problem

• Cell-Free Massive MIMO
• Distributed Beamforming
• Channel Statistics
• MMSE
• Uplink-Downlink Duality

Innovation

• distributed beamforming
• cell-free massive MIMO
• statistics approximation
• GSLI-MMSE
• channel statistics