Comprehensive energy detection framework for intelligent spectrum sensing in cognitive radio networks

Abstract

Cognitive radio networks (CRNs) have emerged as a promising solution to mitigate the spectrum scarcity problem by allowing dynamic and opportunistic spectrum access. Despite popularity, conventional energy detection (ED) methods suffer from noise uncertainty, weak signal sensitivity, and threshold dependency, which degrades the detection accuracy under varying wireless environment. This paper introduces a comprehensive ED framework to address these limitations by developing a flexible evaluation platform for spectrum sensing in CRNs. The proposed framework is analytically modeled to assess various ED approaches and evaluated under diverse channel conditions, including variations in signal-to-noise ratio (SNR), number of unlicensed users (UU), Rice-K factor and noise distributions. The study employs Monte Carlo simulations to validate the detection performance in both centralized and decentralized CRN configurations. The framework introduces a reliable platform to benchmark ED techniques, in which Rice-K factor-based spectrum detection (RKD) method shows significant improvement in detection probability under low-SNR conditions and varying user densities. The distinctiveness appears in its unified analytical approach combining cooperative sensing architectures with channel-aware detection, enables systematic and scalable assessment of ED techniques. In future CRN deployments, this method can be expanded to facilitate advanced channel models and intelligent detection algorithms. It also provides a scalable and useful foundation for reliable spectrum sensing.