  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.
