认知智能电网邻域网络的频谱分配策略

doi:10.11896/jsjkx.190600027

摘要/Abstract

摘要： 可靠、高效的通信网络是充分发挥智能电网潜力的前提。针对智能电网的无线通信环境存在频谱短缺、资源利用效率低等问题,文中将认知无线电技术应用于智能电网的邻域网络通信中,引入认知智能电网概念以保证业务传输的公平性和有效性,考虑了通信过程中的信噪比和路径损耗后,选择网络吞吐量作为信道效益,并在拓扑结构固定的城市居民小区进行建模仿真。在此基础上,提出了一种改进二进制猫群(Weight Binary Cat Swarm Optimization,WBCSO)优化的频谱分配算法。首先,在二进制猫群算法(Binary Cat Swarm Optimization,BCSO)的速度更新公式中加入非线性动态的惯性权重,它随着迭代次数的增加而非线性地递减,以防止算法早熟;其次,引入繁殖算子,产生子代猫群以增加种群的多样性,以获取更好的全局最优解;然后,选用了4个常用的基准函数对改进后的算法进行性能测试,测试结果表明WBCSO算法的优化均值和标准差都优于BCSO算法;最后,以系统总效益和用户公平性为优化目标,将其与二进制遗传算法(Binary Genetic Algorithm,BGA)和二进制粒子群算法(Binary Particle Swarm Optimization,BPSO)进行了对比实验,仿真实验表明,WBCSO算法最终的系统总效益和用户公平性指数比BCSO算法分别高出了13.7%和14.6%,且比二进制群算法和遗传算法的性能都要好,进而表明改进二进制猫群算法在认知智能电网邻域网的频谱分配中具有收敛速度快、搜索能力强的特点。

关键词: 二进制猫群算法, 惯性权重, 邻域网, 频谱分配, 认知智能电网

Abstract: Reliable and efficient communication network is the premise to give full play to the potential of smart grid.In view of the problems existing in the wireless communication environment of smart grid,such as the shortage of spectrum and low efficiency of resource utilization,this paper applied cognitive radio technology to the neighborhood network communication of smart grid.The concept of cognitive smart grid was introduced to ensure the fairness and effectiveness of service transmission.After considering the SNR and path loss in the communication process,the network throughput was selected as the channel benefit,and the modeling and simulation were carried out in urban residential areas with fixed topology.On this basis,an improved spectrum allocation algorithm for binary cat swarm (WBCSO) optimization was proposed.Firstly,the inertia weight of nonlinear dynamics is added into the speed update formula of binary cat swarm algorithm (BCSO),which decreases linearly with the increase of iteration times to prevent premature algorithm.Secondly,a breeding operator is introduced to generate offspring to increase the diversity of the population and obtain a better global optimal solution.Then,four common benchmark functions are selected to test the performance of the improved algorithm.The test results show that the optimized mean and standard deviation of WBCSO algorithm are better than that of BCSO algorithm.With the overall benefit of the system and the fairness of users as the optimization objectives,the proposed algorithm was compared with binary genetic algorithm (BGA) and binary particle swarm optimization (BPSO) on the contrast experiment.The simulation experiments show that WBCSO algorithm eventuallysystem total benefits and user fairness index of WBCSO algorithm is higher than BCSO algorithm with 13.7% and 14.6%respectively,and its performance is better than BPSO and BGA.Therefore,the improved binary cat swarm algorithm has the characteristics of fast convergence and strong search ability in the spectrum allocation of the neighborhood network of cognitive smart power grid.

Key words: Binary cat swarm algorithm, Cognitive smart grid, Inertial weight, Neighborhood network, Spectrum allocation

中图分类号:

TN939

王依柔,张达敏,徐航,宋婷婷,樊英. 认知智能电网邻域网络的频谱分配策略[J]. 计算机科学, 2020, 47(3): 267-272. https://doi.org/10.11896/jsjkx.190600027

WANG Yi-rou,ZHANG Da-min,XU Hang,SONG Ting-ting,FAN Ying. Spectrum Allocation Strategy for Neighborhood Network Based Cognitive Smart Grid[J]. Computer Science, 2020, 47(3): 267-272. https://doi.org/10.11896/jsjkx.190600027

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