SELECTION STRATEGY FOR FULL-SPECTRUM VERSUS LOCAL-SPECTRUM ENERGY DETECTION UNDER CONSTANT FALSE ALARM RATE CONDITIONS

In information science and electrical engineering, energy detection is a foundational tool for dynamic spectrum access and environmental awareness, playing a critical role in modern wireless communication systems. While Local Spectrum Energy Detection (LSED) is commonly regarded as superior to Full Spectrum Energy Detection (FSED), its performance advantage is not guaranteed across all real-world conditions. In this study, we derive closed-form expressions for the false alarm and detection probabilities of both FSED and LSED under the constant false alarm rate (CFAR) criterion. To address varying signal distributions, we develop an analytical framework based on the SNR ratio and categorize the frequency band selection problem into two typical cases: complete and partial capture of signal energy. Monte Carlo simulations validate our theoretical findings and reveal the specific conditions under which LSED provides measurable benefits over FSED, such as in narrowband signal environments or when prior knowledge of the signal's spectral characteristics is available. Furthermore, we propose a novel frequency-band energy ratio metric, which quantifies the relative energy distribution across different bands, enabling adaptive and resource-efficient detection method selection in complex electromagnetic environments. This metric facilitates intelligent switching between FSED and LSED based on real-time spectrum conditions. This work offers theoretical insights and practical guidance for signal detection, spectrum sensing, and data-driven decision-making in electronic communication and IoT-enabled systems, contributing to more efficient spectrum utilization and improved reliability in next-generation wireless networks.