Spectrum and energy efficiency of cooperative spectrum prediction in cognitive radio networks

Abstract

In this paper, the spectrum and energy efficiency of cooperative spectrum prediction (CSP) in cognitive radio networks are investigated. In addition, the performance of cooperative spectrum prediction is evaluated using a hidden Markov model (HMM) and a multilayer perceptron (MLP) neural network. The cooperation between secondary users in predicting the next channel status employs AND, OR and majority rule fusion schemes. These schemes are compared for HMM and MLP predictors as a function of channel occupancy in term of prediction error, spectrum efficiency and energy efficiency. The impact of busy and idle state prediction errors on the spectrum efficiency is also investigated. Simulation results are presented which show a significant improvement in the spectrum efficiency of the secondary users CSP with the majority rule at the cost of a small degradation in energy efficiency compared to single spectrum prediction and traditional spectrum sensing.