One–Bit Compressive Sensing Algorithm for Wideband Spectrum Sensing: A Review

Abstract

Spectrum sensing (SS) is one of the most important techniques in wireless communication for understanding the radio environment. SS techniques, however, are ineffective since they require costly, impractical high-rate analog-to-digital converters (ADCs) for timely communications. However, there are challenges and limitations in traditional spectrum sensing techniques (SSTs), including estimation of sparsity level, selection of the number of measurements, noise uncertainty, receiver uncertainty, sensitivity at low signal-to-noise-ratio (SNR) values, and interference from channel coding. One-bit compression (CS) has been seen as a promising sensing technique that allows extremely easy, efficient, and fast sampling and quantization for wideband spectrum sensing. It can be used in cognitive radio (CR) communication by making use of sparsity in spectrum occupancy brought about by underutilization of the spectrum. In this paper, we provide an overview of compressive spectrum sensing (CSS) algorithms in wideband CR, the current state-of-the-art of CSS in wideband spectrum sensing (WBSS) communication, and its advantages and limitations.