Event Details
Cooperative Spectrum Prediction for Improved Efficiency of Cognitive Radio Networks
Presenter: Nagwa Shaghluf
Supervisor: Prof. T. Aaron Gulliver
Date: Wed, November 15, 2017
Time: 13:30:00 - 14:30:00
Place: EOW 430
ABSTRACT
In this thesis, the spectrum and energy efficiency of cooperative spectrum prediction (CSP) in cognitive radio networks are investigated. In addition, the performance of CSP 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 determined. Further, the spectrum efficiency is compared for different numbers of primary user (PU). Simulation results are presented which show a significant improvement in the spectrum efficiency using CSP with the majority rule at the cost of a small degradation in energy efficiency compared to single spectrum prediction and traditional spectrum sensing.