Optimizing Spectrum Management Using Advanced Spectrum Sensing Techniques

The management of spectrum through the advanced techniques within cognitive radio networks (CRN) is vital for detecting vacant spectrum in networks. With the spectrum dearth increasingly encountered in wireless communication systems, the utilization of dynamic spectrum access represents an important strategy for resource management efficiency. Spectrum sensing plays an important role in finding free frequency bands, thus making it possible to use secondary users without affecting excessively the primary user's operations. The paper includes different advanced techniques, such as Noise-Resilient energy detection (NRED), higher order eigenvalue-based detection (HEVD), and Cyclic Prefix Generalized Likelihood Ratio Test (CGLRT), to optimize their Spectrum Sensing efficiency. The proposed model tests specified hypotheses regarding the presence of a signal providing a robust framework for weak signal detection. Optimizing these techniques improves the correct identification of free frequency bands so that can be opportunistically accessed to meet the network efficiency. The results show efficient performance in terms of probabilities of detection and signal-to-noise ratio where the results of each method are compared. This offers new insights into using vacant spectrum and hence increasing network efficiency.