The power allocation by Particle Swarm Optimization in Dynamic Spectrum Access environments

It is well known that wireless spectrum is a very limited and treasur-able resource for communications. However, the wireless spectrum istruly underutilized in both spectral and time domain so far. Fortu-nately, it has been found that using dynamic spectrum access (DSA)in cognitive radio networks can significantly improve the spectral efficiency by allowing secondary users to access primary channels without interfering primary users. The dynamic spectrum access is not only managing the channel allocation but also power control with the objective to maximize the aggregate throughput of all secondary users. However, when it comes to a multi-user and multi-channel cognitive radio networks condition this problem becomes much more difficult. In the literature this kind of problem is often formulated as a mixed integer nonlinear programming (MINLP), but this problem is an NP-hard problem and it is also hard to solve. Hence, there are some approximation methods proposed to solve this problem which can only led to the suboptimal solutions. Therefore, we propose a novel algorithm to solve this problem and find the optimal solution instead of sub-optimal solution. In the literature [1] , this dynamic spectrum access problem has been carefully reexamined and found that this problem has been over-parameterized. We show that this problemcould be formulated as a nonlinear programming (NLP) without losing globally optimal objective function value. And we can solve this problem by using interior point DSA optimization algorithm in polynomial time. In our paper we use particle swarm optimization (PSO)
to find the optimal solution quick and correct, moreover, by using interior point methods can help us to choose the appropriate region to throw particles which can decrease searching time and cost. Finally, simulation results show performance of our proposed algorithm.