Integrated Distributed Semantic Communication and Over-the-air Computation for Cooperative Spectrum Sensing

TL;DR

This paper introduces an integrated framework combining distributed semantic communication and over-the-air computation to enhance cooperative spectrum sensing, reducing data transmission and improving robustness and scalability.

Contribution

It proposes a novel ICC framework that jointly optimizes sensors and fusion center, achieving improved detection performance and robustness over traditional methods.

Findings

ICC-CSS outperforms conventional CSS schemes in detection accuracy.

The system is robust to SNR variations in sensing and reporting channels.

Extensive simulations demonstrate scalability with more sensors and samples.

Abstract

Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, an integrated communication and computation (ICC) framework is proposed in this paper. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in reporting channel, taking advantage of characteristics of wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.