Research on Energy Detection Methods for Maximizing Spectrum Access Opportunities in Cognitive Radio

By studying cognitive radio technology, we propose an energy detection method designed to maximize spectrum access opportunities. This method involves implementing algorithms that continuously monitor frequency bands through signal energy threshold calculations, where received signal strength is compared against adaptive detection thresholds. The core implementation typically includes Fast Fourier Transform (FFT) processing for frequency domain analysis and statistical decision-making modules for occupancy classification. Key functions involve real-time spectrum sensing with configurable parameters like detection probability and false alarm rates, enabling dynamic threshold adjustment based on noise floor measurements. Through this approach, we thoroughly examine critical concepts of spectrum utilization and radio frequency management to ensure reliability and efficiency in wireless communications. The methodology employs cyclostationary feature detection or matched filtering as complementary techniques to enhance detection accuracy in low SNR scenarios. By implementing this energy detection framework with hardware-in-the-loop testing, we can better understand and address existing spectrum resource constraints, thereby providing expanded spectrum resources for future communication systems. This advancement contributes to driving communication technology development, improving user experience, and fostering innovation in wireless communications through optimized spectrum sharing mechanisms.