A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks

Abstract

Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in cognitive radio networks (CRN). Because of noise uncertainty, the performance of traditional detectors such as matched filters, energy detectors, and even cyclostationary detectors deteriorates rapidly at low Signal-to-Noise Ratios (SNR). To counteract noise uncertainty, a new entropy-based spectrum sensing scheme is introduced in this letter. The entropy of the sensed signal is estimated in the frequency domain with a probability space partitioned into fixed dimensions. It is proven that the proposed scheme is robust against noise uncertainty. Simulation results confirm the robustness of the proposed scheme and show 6dB and 5dB performance improvement compared with energy detectors and cyclostationary detectors, respectively. In addition, the sample size is significantly reduced compared to an energy detector.