Performance Improvement of Energy Detection in Cognitive Radio Under Noise Uncertainty

TLDR: In this paper, the authors compare single and double energy detection algorithms in terms of the characteristics, performance, and simulation results, and analyze the noise effect on the energy detection approach and present opportunistic potential solutions such as Machine Learning (ML) and Graph Signal Processing (GSP) techniques.

Abstract: Spectrum scarcity has been one of the most conspicuous constraints in networking, amidst the growing demand for 5G and Internet of things (IoT) services. Consequently, a cognitive radio networking system (CRN) presents as a reliable solution to counter the current spectrum limitations. Many spectrum sensing techniques have been integrated with CRN to address the spectrum shortage; through the detection of the frequency band and investigating the status of the primary user (PU). Nonetheless, energy detection approaches are perceived as one of the most effective solutions for spectrum sensing, as they can operate in a non-causal manner. Energy detection works with a single or double threshold. Each threshold plays a fundamental role taking into account the stochastic properties of the detected signal and consequently the calculation of the optimal energy level. Despite various research efforts towards improving the energy detection scheme, the proposed algorithms still struggle in coping with noise uncertainty and signal-to-noise ratio (SNR) wall. The main contribution of this paper is the implementation and comparison between a single (conventional) and double energy detection algorithms, in terms of the characteristics, performance, and simulation results. Finally, we analyze the noise effect on the energy detection approach and present opportunistic potential solutions such as Machine learning (ML) and Graph signal processing (GSP) techniques.