摘要
在认知用户和授权用户共存的认知无线网络模型中,为了解决认知无线网络中最大化网络效益和用户间接入网络的公平性联合最优化的多目标频谱分配难题,提出了一种新的基于hooke jeeves(HJ)计算和量子粒子群(quantum particle swarm optimization,QPSO)理论的离散多目标组合优化机制,即HJ-DQPSO优化机制。该机制中,提出了采用HJ算法进行局部搜索,防止陷入局部最优,并对QPSO算法进行离散化处理以便更匹配离散的频谱分配模型。与现有的频谱分配算法进行仿真性能比较,实验结果表明,该机制具有逼近最优解、快速收敛、不易陷入局部最优、参数设置少的特点。在不同的优化目标情况下,能够较好地逼近频谱分配最优解而且可以实现快速收敛,在满足多个优化目标的情况下可以获得更合理的频谱分配方案。
In cognitive radio network model consisting of secondary users and primary users,in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users＇ fairness to access network,this paper proposes a new discrete multi-objective combinatorial optimization mechanism—HJ-DQPSO based on hooke jeeves（ HJ） calculation and quantum particle swarm optimization（ QPSO） algorithm. The mechanism adopts HJ algorithm to local search to prevent falling into the local optimum,and proposes a discrete QPSO algorithm to match the discrete spectrum assignment model. The mechanism has the advantages of approximating optimal solution,rapid convergence,setting less parameters,avoiding falling into local optimum. Compared with existing spectrum assignment algorithms by spectrum assignment algorithm performance simulation,the simulation results show that according to different optimization objectives,the HJ-DQPSO optimization mechanism for multi-objective optimization can better approximate the optimal solution and converge fast. We can obtain a more reasonable spectrum allocation scheme in the case of satisfying multiple optimization objectives.
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2016年第1期37-44,共8页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(61102062)
教育部科学技术研究重点项目(212145)
重庆市科委自然科学基金(CSTC2011JJA1192)
重庆市教委科学技术研究项目(KJ110503)~~
关键词
认知无线电
频谱分配
量子粒子群
多目标优化
cognitive radio
spectrum allocation
quantum particle swarm
multi-objective optimization
