Abstract: Spectrum sensing is an essential ability to detect spectral holes in cognitive radio (CR) networks. The critical challenge to spectrum sensing in the wideband frequency range is how to sense quickly and accurately. Compressive sensing(CS) theory can be employed to detect signals from a small set of non-adaptive, linear measurements without fully recovering the signal. However, the existing compressive detectors can only detect some known deterministic signals and it is not suitable for the time-varying amplitude signal, such as spectrum sensing signals in CR networks. First, a model of signal detect is proposed by utilizing compressive sampling without signal recovery, and then the generalized likelihood ratio test (GLRT) detection algorithm of the time-varying amplitude signal is
derived in detail. Finally, the theoretical detection performance bound and the computation complexity are analyzed. The comparison between the theory and simulation results of signal detection performance over Rayleigh and Rician channel demonstrates the validity of the performance bound. Compared with the reconstructed spectrum sensing detection algorithm, the proposed algorithm greatly reduces the data volume and algorithm complexity for the signal with random amplitudes.

Key words: CR, wideband spectrum sensing, CS, GLRT, time-varying amplitude