Enhancing Cognitive Radio Network Design with New Energy Detection versus Pilot and Radio-Based Techniques

Rizwana N. A () and Nagaraju V ()

SPAST Reports, 2024, vol. 1, issue 3

Abstract: This study aimed to enhance the energy efficiency (EE) and accuracy of the Cognitive Radio Network (CRN)system design by using a unique energy detection approach, contrasting it with the conventional Pilot andRadio Based Detection Technique. A model was developed and processed in Python, using a network datasetfor initial exploration, sourced from the UCI Machine Learning Repository. Statistically, with a confidenceinterval of 95% and sample size of 140, the energy detection's precision was assessed. In evaluating spectrumallocation, the conventional technique had a slightly higher accuracy. However, our proposed energy detectionmethod achieved an impressive 95.2713% accuracy. Surprisingly, it processed in just 4 seconds, half the timetaken by the conventional method. The results confirm the new method's superiority in energy efficiency.

Keywords: Network Dataset; Cognitive Radio Networks; Radio based Detection Technique (search for similar items in EconPapers)
Date: 2024
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