SNR-Adaptive Optimal Threshold Design for Energy Detection in Dynamic Spectrum Access

2026-07-01

AI Summary

This work addresses the limitations of conventional energy detection in dynamic spectrum access, where fixed false-alarm-rate thresholds fail to adapt to time-varying signal-to-noise ratios (SNR), resulting in high misdetection rates. The authors propose an SNR-adaptive optimal threshold design framework that reformulates the total misdetection probability minimization problem into an explicit quadratic optimization form with respect to SNR and sample size. This formulation yields a closed-form analytical solution, eliminating the need for numerical search. The proposed method significantly enhances sensing performance under low-SNR conditions, outperforming both fixed-threshold and constraint-based detection schemes, while also revealing the intrinsic trade-off between false alarms and missed detections.

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.

Problem

• energy detection
• Dynamic Spectrum Access
• SNR-adaptive threshold
• false alarm
• missed detection

Innovation

• SNR-adaptive threshold
• energy detection
• dynamic spectrum access
• closed-form optimization
• error probability minimization