Vinay Gowda
Independent Researcher
India
Abstract
Spectrum sensing is a critical function in cognitive radio networks (CRNs) that enables secondary users to detect vacant licensed channels for opportunistic access without causing harmful interference to primary users. This manuscript presents a comprehensive examination of spectrum sensing techniques prevalent up to 2016, including energy detection, matched filtering, cyclostationary feature detection, waveform-based sensing, and compressive sensing. In addition to theoretical analysis of each method, we discuss representative case studies demonstrating real-world implementations, identify research gaps that persisted through 2016, and propose a methodology for comparative performance evaluation under various channel conditions. Simulation results in terms of probability of detection, probability of false alarm, and receiver operating characteristic (ROC) curves are presented. The findings highlight trade-offs among complexity, sensing time, and detection accuracy, and inform future research directions in robust and low-overhead spectrum sensing.
Keywords
Cognitive radio spectrum sensing techniques
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