JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (1): 62-71.doi: 10.6040/j.issn.1671-9352.0.2023.043

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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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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

CLC Number: 

  • TN929.5

Fig.1

System model"

Table 1

Simulation parameter settings"

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

Fig.2

Relationship between total secondary user throughput and number of secondary users"

Fig.3

Relationship between fairness index of secondary users and power of cognitive base station"

Fig.4

Relationship between average system energy efficiency and number of secondary users"

Fig.5

Relationship between total secondary user throughput and cognitive base station power"

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