An Effective Approach for Cognitive Radio Wideband Spectrum Compressive Sensing over AWGN/Rayleigh Channels

The development of effective and low-power spectrum sensing methods is necessary for the advancement of cognitive radio (CR). Due to high sample rate needed, sensing a broad spectrum faces specific difficulties and problems. Compressive Sensing (CS) framework has been suggested and investigated in numerous recent studies and researches, to increase the effectiveness of sensing of spectrum in wideband CR network and to utilize the radio spectrum’s sparse structure for better and more rapid outcomes. In this paper, a multi-rate asynchronous Sub-Nyquist Sampling system that uses numerous low-rate samplers to accomplish wideband spectrum sensing is proposed. This strategy is best suited for cognitive radio networks because of its low sampling rate. The main benefits of the suggested technique include robustness against the problems with temporal synchronization, energyefficient data sensing, and low complexity implementation. The proposed compressive sensing of Spectrum can be put into practice for recovery capabilities for AWGN and Rayleigh channel’s energy detection and compared with existing techniques like Quantized Soft Decision Based and Modified Gram Schmidt Based Compressive Sensing. Simulation and Performance is measured using variables for Additive White Gaussian Noise (AWGN)/Rayleigh fading conditions at SNR levels with various levels of sparsity including false alarm probability $(\mathrm{P}_{\mathrm{f}\mathrm{a}})$ and detection probability (PD).