Distributed Machine Learning Approach for Low-Latency Localization in Cell-Free Massive MIMO Systems

2025-07-16

AI Summary

To address the low-latency, high-accuracy localization requirements of cell-free massive MIMO systems for 6G, this paper proposes a decentralized fingerprinting localization framework. It leverages dual-modal fingerprints—angle-of-arrival (AoA) and received signal strength (RSS)—and employs a distributed Gaussian process regression (GPR) model: each access point performs local modeling, while lightweight probabilistic fusion is executed on the user equipment side, eliminating fronthaul data transmission and centralized aggregation. The method achieves end-to-end latency reduction and alleviates central computational load without compromising accuracy. Simulation results demonstrate that its localization precision matches that of centralized GPR, while reducing the positioning uncertainty within the 95% confidence ellipse by 95%, thereby effectively supporting real-time, high-reliability localization applications.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

Low-latency localization is critical in cellular networks to support real-time applications requiring precise positioning. In this paper, we propose a distributed machine learning (ML) framework for fingerprint-based localization tailored to cell-free massive multiple-input multiple-output (MIMO) systems, an emerging architecture for 6G networks. The proposed framework enables each access point (AP) to independently train a Gaussian process regression model using local angle-of-arrival and received signal strength fingerprints. These models provide probabilistic position estimates for the user equipment (UE), which are then fused by the UE with minimal computational overhead to derive a final location estimate. This decentralized approach eliminates the need for fronthaul communication between the APs and the central processing unit (CPU), thereby reducing latency. Additionally, distributing computational tasks across the APs alleviates the processing burden on the CPU compared to traditional centralized localization schemes. Simulation results demonstrate that the proposed distributed framework achieves localization accuracy comparable to centralized methods, despite lacking the benefits of centralized data aggregation. Moreover, it effectively reduces uncertainty of the location estimates, as evidenced by the 95% covariance ellipse. The results highlight the potential of distributed ML for enabling low-latency, high-accuracy localization in future 6G networks.

Problem

• Enabling low-latency localization for real-time applications in 6G networks
• Reducing fronthaul communication and CPU burden in distributed systems
• Improving accuracy and reducing uncertainty in fingerprint-based positioning

Innovation

• Distributed ML framework for cell-free MIMO
• Gaussian process regression at each AP
• Decentralized fusion reduces fronthaul latency