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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

33

期数:

2023年01期

页码:

95-100

栏目:

软件技术与工程

出版日期:

2023-01-10

文章信息/Info

Title:

Spectrum Allocation of Cognitive Radio Based on Improved Sparrow Search Algorithm

文章编号:

1673-629X(2023)01-0095-06

作者:

张金飞¹ ; 岳文静¹ ; 陈 志²

1. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;

2. 南京邮电大学 计算机学院,江苏 南京 210023

Author(s):

ZHANG Jin-fei1 ; YUE Wen-jing1 ; CHEN Zhi2

1. School of Telecommunication & Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;

2. School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

关键词:

认知无线电; 麻雀搜索算法; 频谱分配; 透镜成像反向学习; Levy 策略; 系统效益

Keywords:

cognitive radio; sparrow search algorithm; spectrum allocation; lens imaging reverse learning; Levy strategy; system benefit

分类号:

TP301. 6

DOI:

10. 3969 / j. issn. 1673-629X. 2023. 01. 015

摘要:

针对认知无线电中以最大程度提高网络效益为目的的频谱分配问题,提出了一种基于麻雀搜索算法的改进算法。首先,考虑到种群多样性对实验结果的影响,利用透镜成像反向学习策略,在最优个体基础上产生新个体,进而继续寻优,来进一步提高算法的收敛精度;然后,采用变步长设计,在影响步长因素的关键参数中加入步长调整机制,可以调节局部精度和全局最优之间的平衡关系。 再对种群中负责侦查预警的麻雀位置引入 Levy 飞行策略来更新其位置,有助于增强算法跳出局部极值能力和总体寻优的性能。 最后,将改进的麻雀搜索算法应用于认知无线电的频谱分配问题,通过与遗传算法、粒子群算法、海鸥算法及基础麻雀搜索算法进行对比仿真表明,改进的麻雀搜索算法,相比于基础麻雀算法和其他算法,具有更高的网络效益和更快的收敛速度,可以达到有效改善频谱利用率的目的。

Abstract:

An improved algorithm based on sparrow search algorithm is proposed for spectrum allocation in cognitive radio aiming at maximizing network efficiency. Firstly,in view of the effect of population diversity on experimental results,the reverse learning strategy oflens imaging is used,new individuals are generated on the optimal individuals and continue to search for optimization to improve the convergence accuracy of the algorithm. Then, with variable step design, the step adjustment mechanism is added to the key parametersaffecting the step 　　　size factor, which can adjust the balance relationship between local accuracy and global optimization. And theintroduction of Levy flight strategies to update the positions of sparrows in 　　the population responsible for reconnaissance and earlywarning can help enhance the algorithm ’ s ability to jump out of local extremes and overall optimization performance. Finally, an improved sparrow search algorithm is applied to cognitive radio spectrum allocation problem, and compared with genetic algorithm,particle swarm optimization algorithm,seagull algorithm 　and sparrow search algorithm. The results show that the improved sparrow searchalgorithm,compared with the basic sparrow algorithm and other algorithms,has higher network efficiency and faster convergence speed,which can effectively improve the spectrum utilization.

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更新日期/Last Update: 2023-01-10