Multipath Component-Aided Signal Processing for Integrated Sensing and Communication Systems

TL;DR

This paper introduces a novel MPC-aided signal processing approach for integrated sensing and communication systems, utilizing space-time coding to enhance localization accuracy and robustness in dense multipath urban environments.

Contribution

It proposes a symbol-level fusion scheme and a Khatri-Rao space-time code to transform multipath components into beneficial elements, improving ISAC performance.

Findings

SFMC scheme achieves higher localization accuracy.

Proposed methods enhance communication reliability.

Sub-meter level localization in multipath environments.

Abstract

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.