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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (1): 62-71.doi: 10.6040/j.issn.1671-9352.0.2023.043

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基于自适应细菌觅食优化策略的CR-NOMA功率分配算法

彭艺1,2(),马晓霖1,杨青青1,*()   

  1. 1. 昆明理工大学信息工程与自动化学院，云南 昆明 650500
    2. 昆明理工大学云南省计算机技术应用重点实验室，云南 昆明 650500
  • 收稿日期:2023-02-08 出版日期:2024-01-20 发布日期:2024-01-19
  • 通讯作者: 杨青青 E-mail:527037928@qq.com;1016188826@qq.com
  • 作者简介:彭艺(1975—)，女，副教授，博士，研究方向为无线通信等. E-mail: 527037928@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61761025);云南省计算机重点实验室项目(2021102)

Power allocation algorithm for CR-NOMA based on adaptive bacterial foraging optimization strategy

Yi PENG1,2(),Xiaolin MA1,Qingqing YANG1,*()   

  1. 1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
    2. Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • Received:2023-02-08 Online:2024-01-20 Published:2024-01-19
  • Contact: Qingqing YANG E-mail:527037928@qq.com;1016188826@qq.com

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摘要：

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。

关键词: 认知无线电, 非正交多址接入, 功率分配, 细菌觅食

Abstract:

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.

Key words: cognitive radio, non-orthogonal multiple access, power allocation, bacterial foraging

中图分类号: 

  • TN929.5

图1

系统模型"

表1

仿真参数设置"

仿真参数 数值
小区半径/m 500
系统带宽/MHz 5
噪声功率谱密度/(dBm·Hz-1) -174
路径损耗系数 2
电路消耗功率/W 1
次用户与认知基站最小距离/m 40
次用户间最小距离/m 30
次用户数量 10~60
主用户发送功率/dBm 30
主用户数量 10

图2

次用户总吞吐量与次用户数量的关系"

图3

次用户的公平指数与认知基站功率的关系"

图4

平均系统能效与次用户数量的关系"

图5

次用户总吞吐量与认知基站功率的关系"

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