Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

TL;DR

This paper introduces a compressed sensing-based signal processing algorithm for integrated sensing and communication in 6G systems, effectively operating over non-continuous spectrum bands with improved accuracy and reduced sidelobes.

Contribution

The paper proposes a novel high-precision ISAC signal processing method using compressed sensing, integrating 5G NR resource information and employing FISTA and K-fold cross validation for enhanced performance.

Findings

Lower sidelobes with near-zero sidelobe levels.

Up to 54.66% improvement in range estimation accuracy.

Up to 97.09% improvement in velocity estimation accuracy.

Abstract

As a promising key technology of 6th generation (6G) mobile communication system, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands. However, the conventional sensing algorithms over non-continuous spectrum bands have disadvantages such as reduced peak-to-side lobe ratio (PSLR) and degraded anti-noise performance. Facing this challenge, we propose a high-precision ISAC signal processing algorithm based on compressed sensing (CS) in this paper. By integrating the resource block group (RBG) configuration information in 5th generation new radio (5G NR) and channel information matrices, we can dynamically and accurately obtain power estimation spectra. Moreover, we employ the fast iterative shrinkage-thresholding algorithm (FISTA) to address the reconstruction problem and utilize K-fold cross validation (KCV) to obtain optimal parameters. Simulation results show that the proposed algorithm has lower sidelobes or even zero sidelobes compared with conventional sensing algorithms. Meanwhile, compared with the improved 2D FFT algorithm and conventional 2D FFT algorithm, the proposed algorithms in this paper have a maximum improvement of 54.66 % and 84.36 % in range estimation accuracy, and 41.54 % and 97.09 % in velocity estimation accuracy, respectively.