Intelligent dynamic spectrum access exploiting a synergy between genetic algorithm and local search

Abstract

This thesis presents a novel hybrid dynamic spectrum access technique for multi-channel single-radio cognitive radio networks. Existing classical and stochastic approaches exhibit di erent advantages and disadvantages depending on network topology and architecture. Our proposed approach exploits a delicate balance between these two types of approaches for extracting advantages from both of them while limiting their disadvantages. We exploit a synergy between genetic algorithm-based stochastic search and classical local search to design a highly scalable and e cient dynamic spectrum access technique. Additionally, we boost up the performance of our algorithm through designing new genetic operators. Besides, proper and thorough performance evaluation of existing approaches using a discrete event simulator is yet to be performed in the literature. To address this issue, we simulate several existing approaches using a widely used discrete event simulator called ns-2. We evaluate the performance of our proposed technique in ns-2 on the basis of various standard performance metrics. In the evaluation, we compare the performance of our proposed technique with that of the state-of-the-art approaches. Simulation results demonstrate signi cant performance improvement using our proposed approach over the existing ones.