Entropy augmented energy detection for cognitive radio: robust spectrum sensing under fading channels

Abstract

Cognitive radio systems provide an intelligent solution to spectrum scarcity by dynamically accessing underutilized frequency bands licensed to primary users. Among various sensing techniques, energy detection (ED) is extensively adopted due to its inherent simplicity and minimal computational requirements, but suffers from poor performance at low signal-to-noise ratio (SNR) environments under channel impairments such as additive white gaussian noise (AWGN) and multipath fading. This paper proposes an enhanced spectrum sensing approach by integrating ED with entropy-based techniques, specifically Kapur and Renyi entropy measures. The proposed methods are evaluated under AWGN and various fading environments with binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations. Simulation results demonstrate substantial improvements in detection performance. The results show that entropy-based enhancements significantly improve the reliability of spectrum sensing in cognitive radio (CR) systems operating under challenging channel conditions. Among the fading models, the Nakagami channel causes the greatest degradation in detection probability, followed by the Rayleigh fading channel. ED with Renyi entropy improves P_d by 15-fold and 8-fold, compared to ED under Nakagami and Rayleigh channels respectively.