Real-Time Bayesian Neural Networks for 6G Cooperative Positioning and Tracking

Abstract

In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.