Performance Analysis of Sub-Nyquist Wideband Spectrum Sensing over Fading Channels

This paper studies the performance of wideband spectrum sensing (WBSS) with sub-Nyquist sampling in cognitive networks. Considering the limitations posed by expensive and power-hungry analog-to-digital converters for Nyquist sampling approaches and high-computational complexity for compressed sensing approaches, this paper focuses on performing WBSS directly from aliased subbands with limited samples in a cost-effective and computationally efficient manner, and understanding the relationship between downsampling rates and sensing performance. To address the aliasing effect caused by sub-Nyquist sampling, bin detection is used to evaluate the channel occupancy. Moreover, the sensing performance is analyzed over fading channels in terms of probabilities of miss detection, false alarm, insufficient spectrum opportunity, and excessive interference opportunity. The results show that sub-Nyquist sampling can achieve WBSS without full spectrum recovery, thus reducing the sensing overhead and computational complexity.