Symbol-level Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing

TL;DR

This paper introduces a symbol-level cooperative sensing method for multi-base station mobile communication systems, significantly enhancing target localization accuracy with lower synchronization requirements, suitable for smart city and transportation applications.

Contribution

It proposes a novel symbol-level data fusion approach for cooperative sensing, improving accuracy and reducing synchronization needs compared to existing methods.

Findings

Distance estimation accuracy improved by 40% at -5 dB SNR.

Velocity estimation accuracy improved by 72% at -5 dB SNR.

Outperforms single-BS sensing and data-level fusion in accuracy.

Abstract

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This paper proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is -5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system.