认知无线网络中基于免疫优化的比例公平资源分配

针对认知无线网络中基于OFDM技术的资源分配,将其建模为一个约束优化问题,进而提出了一种基于免疫克隆的求解方法. 算法采用两阶段资源分配方法,即先将子载波分配给用户,然后基于免疫优化算法给不同的子载波分配功率. 此外,算法充分考虑了主用户可容忍的干扰约束及次用户对资源的比例需求,更符合实际要求. 根据问题本身特点,设计了适合算法求解的编码、克隆、变异算子. 仿真实验结果表明,在总发射功率、误码率及主用户可接受的干扰等约束下,本算法可以获得较高的数据吞吐量,并保证次用户对资源需求的公平性. 

Immune-Based Resource Allocation with Proportional Fairness in Cognitive Wireless Network

The solution of resource allocation of OFDM-based cognitive wireless network could be formulated into an optimization problem with constraints. An immune clonal algorithm is proposed to solve the problem. The resource allocation is divided into two steps. First, the subcarriers are allocated to secondary users. Second, the immune-based algorithm is employed to allocate power for subcarriers. Moreover, the proposed algorithm fully takes into account the interference that primary user can tolerate and the proportional fair demands for secondary users. The suitable operators, such as coding, clonal, mutation, are designed for solving the problem. Experiments results show that, subject to the constraints of total power, BER (bit error rate) and the acceptable interferences of primary user, the proposed algorithm obtains high system throughout with proportional fairness among the secondary users. Both theoretical analysis and simulation results show the effectiveness of the proposed algorithm.