Multiple Signal Classification Based Joint Communication and Sensing System

TL;DR

This paper introduces a MUSIC-based joint communication and sensing system that significantly improves sensing accuracy and channel estimation in mobile networks compared to traditional FFT-based methods, with theoretical and simulation validation.

Contribution

It proposes a novel MUSIC-based JCS system for enhanced sensing accuracy and a CSI enhancement method, surpassing FFT-based approaches in performance.

Findings

MUSIC-based JCS achieves over 20 dB lower sensing MSE than FFT-based methods.

Proposed CSI enhancement reduces bit error rate in communication.

Theoretical lower bounds for sensing MSE are derived.

Abstract

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.