Power allocation algorithm for CR-NOMA based on adaptive bacterial foraging optimization strategy
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针对多个主次用户场景中采取 underlay 模式的认知无线电非正交多址接入(cognitive radio non-orthogonal multiple access,CR-NOMA)系统的低频谱利用率问题,提出一种基于自适应细菌觅食优化策略的功率分配算法.首先进行联合用户匹配,将次用户分组问题等效为次用户-子信道双向动态匹配问题.其次,构造次用户功率比例因子向量并将其映射为细菌个体的位置向量,在趋向操作中改进细菌游动步长、旋转方向;复制操作中结合差分进化算法对前半数优质解进行变异选择;迁徙操作中定义迁徙范围,采用自适应迁徙概率,加快寻找最佳位置向量进程.最后得到最佳功率比例因子以最大化系统总吞吐量.结果表明,本文所提算法与层级配对功率分配(hierarchical pairing power allocation,HPPA)算法和CR-OMA算法相比,能够有效加快收敛速度,增强全局寻优能力,具有更好的系统性能.
A power allocation algorithm based on an adaptive bacterial foraging optimization strategy is proposed to aim at the problem of low spectrum utilization of cognitive radio non-orthogonal multiple access(CR-NOMA)system with underlay mode in multiple primary and secondary user scenarios,Firstly,the joint user matching is carried out,and the secondary user grouping problem is equivalent to the secondary user-subchannel bidirectional dynamic matching problem.Secondly,the power scale factor vector of the secondary user is constructed and mapped into the position vector of the bacterial individual,and the bacterial swimming step and rotation direction are improved in the trend operation.In the replication operation,the differential evolution algorithm is used to perform mutation selection on the first half of the high-quality solutions.In the migration operation,the migration range is defined,and the adaptive migration probability is used to speed up the process of finding the best position vector.Finally,the optimal power scaling factor is obtained to maximize the total throughput of the system.The results show that compared with the hierarchical pairing power allocation(HPPA)algorithm and the CR-OMA algorithm,the proposed algorithm can effectively accelerate the convergence speed,enhance the global optimization ability,and have better system performance.
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