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

TL;DR

This paper introduces a distributed machine learning framework for low-latency, high-accuracy localization in cell-free massive MIMO systems, reducing communication overhead and computational load while maintaining performance.

Contribution

It presents a novel decentralized ML approach using local Gaussian process models at access points for efficient, low-latency localization in 6G networks.

Findings

Achieves localization accuracy comparable to centralized methods

Reduces latency by eliminating fronthaul communication

Decreases uncertainty in position estimates

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.