Adaptive Resource Allocation for Cognitive Radio Networks with Multiple Primary Networks

In this article, adaptive resource allocation (ARA) is investigated for multiple primary networks based-cognitive radio networks under a more practical system model, where the bandwidth of each secondary user is assumed to be limited and the maximum allowable interference for each primary network is different. We first formulate the ARA as a constrained optimization problem with the objective function of maximizing the proportional fairness-based ergodic sum capacity. The multiple constraints optimization problem is NP-hard and therefore, we propose a scheme to decompose the optimization problem into two unconstrained optimization problems by designing alternative objective functions and penalty functions. Then, a suboptimal heuristic solution framework based on particle swarm optimization is proposed to solve the unconstrained optimization problems. Computation simulations are carried out and the results show that the proposed scheme outperforms traditional ARA schemes.