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针对认知无线电网络中干扰温度下的吞吐量调度问题,基于问题的NP-hard特性,提出一种基于智能免疫优化的次优吞吐量调度算法.将吞吐量调度问题建模为一个最大化所有认知用户吞吐量的约束优化问题,给出了吞吐量调度问题和免疫算法的映射关系,设计了适合问题求解的二进制抗体编码方式、基于先验知识的抗体初始化方法、基于抗体亲和度的比例克隆方式及基于进化代数的变异算子.实验结果表明,所提算法可以得到大约95%的最优吞吐量,并且具有较低的线性复杂度.
Aimed at the problem of throughput scheduling under interference temperature in cognitive radio networks, a suboptimal scheduling algorithm based on intelligent NP-hard optimization is proposed based on the NP-hard characteristic of the problem. The throughput scheduling problem is modeled as a maximized The constrained optimization of cognitive user throughput is given. The mapping between throughput scheduling problem and immune algorithm is given. The binary antibody coding method is designed to solve the problem. The antibody initialization method based on prior knowledge is designed. Based on the antibody affinity Proportional cloning method and mutation operator based on evolution algebra.Experimental results show that the proposed algorithm can get about 95% of the optimal throughput and has a lower linear complexity.