Enhanced Signal Energy Detection Technique for Low SNR Spectrum Sensing in Cognitive Radio – Hybrid Approach

Identification of Primary User (PU) signals in the context of Cognitive Radio (CR) networks essentially calls for an efficient spectrum sensing methodology. Even though the mainstream Energy Detection technique has several severe drawbacks, specifically at low SNR environments that impair effective detection of PU signals, this article presents a novel hybrid methodology consisting of the ESED technique. The integration of wavelet denoising with adaptive thresholding effectively mitigates the shortcomings of traditional ED. Simulation results are presented and clearly show that ESED technique offers high detection accuracy with moderate computational complexity. It is a more favourable approach to resource-limited CR devices that enhance the reliability of spectrum sensing in low SNR conditions. This new method has outstanding performance. In particular, for probability of detection (Pd) equal to 1 at an SNR of -10 dB, it achieved a detection probability (Pd) of 0.8 at an SNR of -20 dB. High detection probabilities under low SNR circumstances cannot be attained with the use of the traditional method as observed from comparisons. Consequently, this method effectively identifies Primary Users (PUs) and optimizes spectrum utilization, thereby enhancing spectrum management, reducing interference, and ensuring the efficient allocation of resources.