Cognitive Radio IOT Networks With Adaptivesensing of The Spectrum

Abstract

One of the most important problems in Cognitive Radio IoT networks (CRIOT) for allowing Dynamic Spectrum Access (DSA) among secondary users is spectrum sensing (SUs). Detection probability, average detection time, throughput, and false alarm probability all affect spectrum sensing performance in CRIOT networks.We propose an adaptive spectrum sensing technique that adaptively picks between a Matched Filter  detector(MF) or a Hidden Markov Model (HMM) based sequence detector to increase spectrum sensing performance. When Signal to Noise Ratio(SNR) is strong, this adaptive sensing technique chooses Matched Filter  detector and when the channel SNR is low, it chooses a HMM-based sequence detector. This adaptive sensing methodology improves time by selecting the method of detectiondepending on the received SNR. It has higher spectrum sensing performance in low and high SNR regions, and its performance has been independently tested.