An Improved Optimal Channel Sensing Algorithm in Cognitive Radio Networks Used for Video Surveillance
Ranjita Joon, Parul Tomar · Journal of Mobile Multimedia · 2022
With a rapid rise in the number of wireless devices and gadgets, a shortage in the spectrum bands for wireless communications has been observed. To overcome this problem of shortage of spectrum bands, a new technology called Cognitive Radio Networks (CRNs) was adopted. CRNs help us utilize the spectrum bands which are currently being underutilized by opportunistically and intelligently switching to these underutilized white spaces. Thus, CRNs aim to use the frequency spectrum in an opportunistic manner by allowing different users to operate in available frequency bands without interference. In this paper, Double Q Learning (DQN) with prioritized experience relay approach has been used to study the throughput of the network at different parameters and to draw a relationship between throughput and Probability of Undetectable User Transmission. Double Q Learning (DQN) with prioritized experience relay is a reinforcement learning based method that adds backward exploration to the forward exploration of Q Learning method. Both forward and backward exploration are used to update the Q values. Since the sensor nodes in the cognitive environment have limited energy, and sensing the spectrum band involves energy consumption, so the technique for sensing should be energy efficient so that the sensor nodes can be effectively used for various operations of video surveillance.