Simultaneous Source Separation, Synchronization, Localization and Mapping for 6G Systems

TL;DR

This paper introduces a joint Bayesian framework for simultaneous source separation, synchronization, and mapping in 6G systems, addressing practical issues like interference and synchronization errors, and demonstrating robustness comparable to ideal assumptions.

Contribution

It proposes a novel BS-dependent data association and synchronization bias model integrated into a Bayesian framework, relaxing ideal assumptions in cooperative MP-SLAM.

Findings

Joint synchronization and source separation have minimal impact on performance.

The proposed model effectively classifies features by persistent properties.

Robustness is maintained even with interference and synchronization errors.

Abstract

Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach for future 6G networks to jointly estimate the positions of transmitters and receivers together with the propagation environment. In cooperative MP-SLAM, information collected by multiple mobile-terminals (MTs) is fused to enhance accuracy and robustness. Existing methods, however, typically assume perfectly synchronized base stations (BSs) and orthogonal transmission sequences, rendering inter-BS interference at the MT negligible. In this work, we relax these assumptions and address simultaneous source separation, synchronization, and mapping. A relevant example arises in modern 5G systems, where BSs employ muting patterns to mitigate interference, yet localization performance still degrades. We propose a novel BS-dependent data association and synchronization bias model, integrated into a joint Bayesian framework and inferred via the sum-product algorithm on a factor graph. The impact of joint synchronization and source separation is analyzed under various system configurations. Compared with state-of-the-art cooperative MP-SLAM assuming orthogonal and synchronized BSs, our statistical analysis shows no significant performance degradation. The proposed BS-dependent data association model constitutes a principled approach for classifying features by arbitrary properties that persist over time, such as reflection order or feature type (scatter points versus walls).