Autonomous Compressive-Sensing-Augmented Spectrum Sensing

Abstract

This paper proposes a new spectrum sensing technique, referred to as autonomous compressive sensing (CS)-augmented spectrum sensing, which can be developed to provide more efficient spectrum opportunity identification than geolocation database methods. First, we propose an autonomous CS-based sensing algorithm that enables the local secondary users (SUs) to automatically choose the minimum sensing time without knowledge of spectral sparsity or channel characteristics. The compressive samples are collected block-by-block in time, while the spectral is gradually reconstructed until the proposed stopping criterion is reached. Moreover, a CS-based blind cooperating user selection algorithm is proposed to select the cooperating SUs via indirectly measuring the degeneration of the signal-to-noise ratio experienced by different SUs. Numerical and real-world test results demonstrate that the proposed algorithms achieve high detection performance with reduced sensing time and number of cooperating SUs in comparison with the conventional compressive spectrum sensing algorithms.