基于遗传粒子群优化算法的认知无线电系统功率分配

doi:10.13229/j.cnki.jdxbgxb20180844

摘要/Abstract

摘要：

考虑授权用户的干扰功率阈值和认知用户的信干噪比（Signal to interference plus noise ratio，SINR）要求，提出了一种基于遗传思想的粒子群优化（Genetic particle swarm optimization，GPSO）算法，研究认知用户发射功率最小化的问题。GPSO算法在适应度值计算、速度更新和位置更新阶段引入选择、交叉和变异操作。仿真结果表明，与拉格朗日乘子法和粒子群优化（Particle swarm optimization，PSO）算法相比，GPSO算法降低了发射功率并获得了更高的SINR。

关键词: 通信技术, 认知无线电网络, 功率控制, 粒子群优化算法, 遗传算法

Abstract:

Considering the requirements of the interference power threshold for the primary users and the signal to interference plus noise ratio (SINR) for the secondary users, this paper proposes a particle swarm optimization based on genetic thought, namely genetic particle swarm optimization (GPSO). This scheme studies the issue of minimizing secondary users' transmit power in the CRN. The GPSO algorithm introduces selection, crossover, and mutation operations in the fitness value calculation, speed update, and location update phases. Compared with the Lagrange multiplier method and PSO, the GPSO algorithm reduces the transmit power and obtains a higher SINR.

Key words: communication technology, cognitive radio networks, power control, particle swarm optimization(PSO) algorithm, genetic algorithm

中图分类号:

TN929.5

王宏志,姜方达,周明月. 基于遗传粒子群优化算法的认知无线电系统功率分配[J]. 吉林大学学报(工学版), 2019, 49(4): 1363-1368.

Hong⁃zhi WANG,Fang⁃da JIANG,Ming⁃yue ZHOU. Power allocation of cognitive radio system based on genetic particle swarm optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1363-1368.

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参考文献 18

1	Chen S , Alemseged Y D , Ha N T , et al . Power control of cognitive radio system in rayleigh fading[C]∥IEEE Global Telecommunications Conference, Honolulu, HI, USA, 2009: 319⁃324.
2	Haykin S . Cognitive radio: brain⁃empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201⁃220.
3	Qu Y , Wang M , Hu J . A new energy⁃efficient scheduling algorithm based on particle swarm optimization for cognitive radio networks[C]∥Proceedings of IEEE International Conference on Signal Processing, Communications and Computing, Guilin, China, 2014: 467⁃472.
4	Gkionis G , Sgora A , Vergados D D , et al . An effective spectrum handoff scheme for cognitive radio ad hoc networks[C]∥Proceedings of Wireless Telecommunications Symposium, Chicago, IL, 2017: 1⁃7.
5	Mahdi A H , Mohanan J , Kalil M A , et al . Adaptive discrete particle swarm optimization for cognitive radios[C]∥Proceedings of IEEE International Conference on Communications, Ottawa, Canada, 2012: 6550⁃6554.
6	Zhao Z , Xu S , Zheng S , et al . Cognitive radio adaptation using particle swarm optimization[J]. Wireless Communications and Mobile Computing, 2009, 9(7): 875⁃881.
7	Sun J , Palade V , Wu X J , et al . Solving the power economic dispatch problem with generator constraints by random drift particle swarm optimization[J]. IEEE Transactions on Industrial Informatics, 2013, 10(1): 222⁃232.
8	Gesbert D , Hanly S , Huang H , et al . Multi⁃cell mimo cooperative networks: a new look at interference[J]. IEEE Journal on Selected Areas in Communications, 2010, 28(9): 1380⁃1408.
9	Zhang Q , Wan P , Wang Y , et al . A spectrum allocation method based on random drift swarm optimization algorithm[C]∥Proceedings of International Conference on Communication Software and Networks, Guangzhou, China, 2017: 289⁃295.
10	栾磊, 赵晓晖, 徐勇军 . 面向区域的认知无线电系统的频谱感知模型[J]. 吉林大学学报:工学版, 2016, 46(4): 1304⁃1312.
	Luan Lei , Zhao Xiao⁃hui , Xu Yong⁃jun . Spectrum sensing model of region⁃oriented cognitive radio system[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(4): 1304⁃1312.
11	Zhang H , Nie Y , Cheng J , et al . Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing[J]. IEEE Transactions on Wireless Communications, 2017, 16(2): 730⁃743.
12	Zhang E , Chen Q . Multi⁃objective reliability redundancy allocation in an interval environment using particle swarm optimization[J]. Reliability Engineering and System Safety, 2016, 145:83⁃92.
13	Wang B , Wang S , Zhou X Z , et al .Two⁃stage multi⁃objective unit commitment optimization under hybrid uncertainties[J]. IEEE Transactions on Power Systems, 2016, 31(3): 2266⁃2277.
14	Ratnaweera A , Halgamuge S K , Watson H C . Self⁃organizing Hierarchical Particle Swarm Optimizer with Time⁃varying Acceleration Coefficients[M]. New York: IEEE Press, 2004.
15	Tiwari P , Saha S . Co⁃channel interference constrained spectrum allocation with simultaneous power and network capacity optimization using PSO in cognitive radio network[C]∥Proceedings of IEEE International Conference on Advanced Networks and Telecommunications Systems, Kolkata, India, 2016:1⁃3.
16	Tuan P V , Koo I . Robust weighted sum harvested energy maximization for SWIPT cognitive radio networks based on particle swarm optimization[J]. Sensors, 2017, 17(10): 2275.
17	Lobiyal D K , Katti C P , Giri A K . Parameter value optimization of Ad⁃hoc on demand multipath distance vector routing using particle swarm optimization[J]. Procedia Computer Science, 2015, 46: 151⁃158.
18	Starke S , Hendrich N , Magg S , et al . An efficient hybridization of genetic algorithms and particle swarm optimization for inverse kinematics[C]∥Proceedings of IEEE International Conference on Robotics and Biomimetics, Qingdao, China, 2017: 1782⁃1789.

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