M-QAM Dynamic Threshold based MFD in CRN for beyond 5G Applications

Despite the limitations of 5G, cognitive radio networks are evolving due to the constant need for faster data rates and greater connectivity in contemporary wireless communication systems. Efficient spectrum sensing becomes crucial as we traverse the unexplored regions of these sophisticated networks to guarantee the best possible use of the constrained and congested radio frequency spectrum. For cognitive radio to employ spectrum resources effectively, spectrum sensing techniques are used. MATLAB has been utilized to evaluate the performance of matched filter detection, utilizing dynamic thresholds in this research study. In this paper we have compared results simulated in Rician and Log - Normal (LN) Fading channel Distribution with AWGN channel for different QAM modulation orders, such as 4, 8, 16, 64, and 256 QAM. ROC curve is used for monitoring the probability of detection (Pd) which is obtained for a variety of SNR ranges and under different probability of false alarms (Pf) conditions. Based on the results, it is observed that the probability of detection for higher order QAM is higher for a larger number of sample values than for a lower-order modulation of QAM. We have proposed Dynamic Threshold in different channel scenarios to enhance the decision reliability. For a probability of false alarm (Pf) of 0.01, the corresponding Pd is determined to be 0.88 for 64- QAM and for 256-QAM, Pd is found to be 0.99 in Rician channel. On comparison with Log Normal channel, Rician channel performs better in noisy conditions at low SNR.