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Cooperative spectrum sensing (CSS) is a promising approach to improve thedetection of primary users (PUs) using multiple sensors. However, there areseveral challenges for existing combination methods, i.e., performancedegradation and ceiling effect for hard-decision fusion (HDF), as well assignificant uploading latency and non-robustness to noise in the reportingchannel for soft-data fusion (SDF). To address these issues, in this paper, wepropose a novel framework for CSS that integrates communication andcomputation, namely ICC. Specifically, distributed semantic communication (DSC)jointly optimizes multiple sensors and the fusion center to minimize thetransmitted data without degrading detection performance. Moreover,over-the-air computation (AirComp) is utilized to further reduce spectrumoccupation in the reporting channel, taking advantage of the characteristics ofthe wireless channel to enable data aggregation. Under the ICC framework, aparticular system, namely ICC-CSS, is designed and implemented, which istheoretically proved to be equivalent to the optimal estimator-correlator (E-C)detector with equal gain SDF when the PU signal samples are independent andidentically distributed. Extensive simulations verify the superiority ofICC-CSS compared with various conventional CSS schemes in terms of detectionperformance, robustness to SNR variations in both the sensing and reportingchannels, as well as scalability with respect to the number of samples andsensors.

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