Genetic algorithm-based scheduling in cognitive radio networks under interference temperature constraints

The proliferation of wireless technologies and services has intensified the demand for the radio spectrum. However, the currently existing fixed spectrum assignment policy leads to an inefficient and unevenly distributed spectrum utilization. Cognitive radio paradigm has been proposed to alleviate these drawbacks by employing dynamic spectrum access (DSA) methodology. Federal Communications Commission (FCC) has proposed the interference temperature model, which enables the unlicensed users to utilize the licensed frequencies simultaneously with the licensed users as long as they conform to the interference temperature constraints. Recently, throughput and delay optimal schedulers that meet the interference temperature constraints in cognitive radio networks have been formulated in the literature. However, these schedulers have high computational complexity. In this paper, we propose genetic algorithm (GA)-based suboptimal methods addressing these throughput and delay optimal scheduling problems. The simulation results corroborate that our GA-based approach yields very close performance to the optimal solutions and operates with much lower complexity. Copyright (C) 2010 John Wiley & Sons, Ltd.