SNR-Adaptive Optimal Threshold Design for Energy Detection in Dynamic Spectrum Access
2026-07-01 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors propose a new way to choose the detection threshold for finding available wireless channels that changes based on signal quality (SNR). Instead of using fixed rules that only limit false alarms, their method aims to minimize mistakes overall by deriving a simple formula. This allows the system to adjust thresholds dynamically without complex computations, especially helping in tough conditions with weak signals. Their tests show this reduces errors compared to older methods and helps balance missing real signals and raising false alarms. The work also sets a base for improving collaborative sensing using technologies like blockchain.
energy detectiondynamic spectrum accesssignal-to-noise ratio (SNR)false alarmmissed detectionthreshold optimizationconstant false alarm rate (CFAR)cooperative spectrum sensingblockchainquadratic optimization
Authors
Sushila Dhaka, Jane-Hwa Huang, Chin-Min Yu, Li-Chun Wang
Abstract
This paper proposes an SNR-adaptive optimal threshold design framework for energy detection in Dynamic Spectrum Access (DSA). Unlike conventional constant false-alarm rate (CFAR)-based schemes that determine the sensing threshold solely from a predefined false-alarm constraint, the proposed method directly minimizes the total probability of error by deriving a closed-form analytical solution. The threshold optimization problem is formulated as a quadratic expression whose coefficients explicitly characterize the effects of signal-to-noise ratio (SNR) and number of samples. This analytical structure enables adaptive threshold selection under heterogeneous SNR conditions without exhaustive numerical search. Simulation results demonstrate that the proposed approach reduces the error probability compared with fixed-threshold and detection-constrained schemes, particularly in low-SNR regimes. Furthermore, the impact of SNR and number of samples on detection performance is systematically analyzed, providing deeper insight into the trade-off between false alarm and missed detection. The proposed framework improves sensing reliability and practical adaptability in dynamic spectrum access systems. It also establishes a foundation for secure cooperative spectrum sensing, including blockchain-assisted aggregation mechanisms.