An Efficient Compressive Spectrum Sensing Technique for Cognitive Radio System

This paper presents a compressive sampling technique incorporated with feature learning to achieve highly effective and flexible spectrum sensing for hybrid cognitive radio operation using the combination of underlay and interweave transmission modes. Leading eigenvectors are first extracted from compressively sampled training sets of primary signals, whose learned features are then used for signal detection based on the general likelihood ratio test. Compared to existing approaches that are typically non-blind and operate at Nyquist sampling rate, simulation results based on IEEE 802.22 WRAN environment show that the proposed technique is able to achieve higher transmission throughput, while operating at 17% of Nyquist sampling rate performed over a shorter spectrum sensing duration.