混沌免疫算法求解认知无线电网络资源分配问题

Chaotic immune optimization based resource allocation in cognitive radio network

为了优化认知无线电网络中多用户正交频分复用子载波的资源分配,将其转换为一个约束优化问题,进而提出了一种基于混沌免疫优化的求解方法.给出了算法的实现过程和关键技术,设计了适合算法求解的编码、克隆、重组、变异算子.实验结果表明,在满足认知用户速率、所需误码率及干扰约束的条件下,本文所用算法减小了整个系统所需的总发射功率,同时收敛速度较快,能够得到较优的子载波分配方案,进而提高频谱利用效率.

In order to optimize the multi-user subcarrier allocation of cognitive wireless network, it is converted into a constraint optimization problem. A chaotic immune optimization algorithm is proposed to solve it. The key techniques and implementation processes are given. The operators, such as coding, clonal, crossover, and mutation, are designed. The experimental results show that in conditions of user rate, the bit error rate and inference constraints, the algorithm minimizes the total transmit power and converges rapidly. It can obtain the better allocation scheme and improve the utilization efficiency of high frequency spectrum.