Bayesian analysis of spectrum occupancy prediction in cognitive radio
Jaison Jacob, Babita Roslind Jose, Jimson Mathew · Smart Science · 2016
Efficient spectrum sensing is an important requirement for the success of the cognitive radio system. Presence of primary users over a specific band has to be monitored periodically in each time slot. Throughput of the system can be improved by sensing only those channels with higher probability of being idle. In this paper, we suggest a prediction-based spectrum sensing scheme and propose two simple and fast approaches based on Bayesian inference to predict the probability of a busy/idle next state. Further analysis is done to study the impact of various parameters associated with them.This channel prediction will help to select suitable channels for spectrum sensing from a rank list prepared based on the probability of channel being idle. It is seen that channel ranking using Bayesian approaches closely follow actual ranking. Proposed approaches are compared for their prediction performance and the computational complexity, with other approaches based on EWMA, HMM, and Neural Networks, which are already available in the literature. Data from spectrum occupancy measurement are used to compare the performance of all the above methods. It is seen that Bayesian approaches are having low computational complexity and hence faster. Their performance is also superior to other methods and it establishes that Bayesian methods are potential candidates for spectrum prediction in a realistic scenario.