Solving Port Selection Problem in Multiple Beam Antenna Satellite Communication System by Using Differential Evolution Algorithm

被引:9
作者
Ding, Yang [1 ]
Jiao, Yong-Chang [1 ]
Zhang, Li [1 ]
Li, Biao [1 ]
机构
[1] Xidian Univ, Natl Key Lab Antennas & Microwave Technol, Xian 710071, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Combinatorial optimization problem; differential evolution; hybrid matrix power amplifier; multiple beam antennas; OPTIMIZATION; DESIGN;
D O I
10.1109/TAP.2014.2341293
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Hybrid matrix power amplifier (HMPA) has important applications in the multi-beam antenna satellite communication systems. The fabrication and port selection complexity of the HMPA increases with the number of feed ports, thus the relatively large HMPA is always substituted by several small HMPAs in engineering applications. In spite of this, determining the port connection sequence between the HMPA and the beam forming network (BFN) becomes a difficult problem. When the number of feed element is large, improper port selection could cause the uneven power load among small HMPAs, which deteriorates the performance of the satellite system. An interesting method for deriving the port connection sequence via the differential evolution algorithm is proposed in this communication. The approach reorganizes the port selection problem as a combinatorial optimization problem. The differential evolution algorithm is used to determine the optimal sequence applied in the multi-beam antenna system for yielding the near uniform power load among the individual power amplifiers. Simulated results on instances of the 12 and 64 feed elements are presented to illustrate the capabilities and effectiveness of the proposed method for the port selection problem.
引用
收藏
页码:5357 / 5361
页数:6
相关论文
共 50 条
[41]   Dual-Mode Navigation Satellite Selection Algorithm Based on Differential Evolution and Geometry [J].
Zhu J. ;
Xu S.-J. ;
Li K. .
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2021, 44 (03) :9-14
[42]   Solving steel coil ship stowage-planning problem using hybrid differential evolution [J].
Dong, Yun ;
Zhao, Ren .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2019, 57 (18) :5767-5786
[43]   Differential Evolution Algorithm for Solving Coil Scheduling Problem in Parallel Continuous Annealing Lines [J].
Zhao, Shengnan ;
Yang, Yang .
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), 2016, :3417-3422
[44]   A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem [J].
Draa, Amer ;
Meshoul, Souham ;
Talbi, Hichem ;
Batouche, Mohamed .
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2010, 7 (01) :21-27
[45]   A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem [J].
Zhong, Jing-Hui ;
Shen, Meie ;
Zhang, Jun ;
Chung, Henry Shu-Hung ;
Shi, Yu-Hui ;
Li, Yun .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2013, 17 (04) :512-527
[46]   Antenna-array pattern nulling using a differential evolution algorithm [J].
Yang, SW ;
Gan, YB ;
Qing, AY .
INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, 2004, 14 (01) :57-63
[47]   Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm [J].
Gurarslan, Gurhan ;
Karahan, Halil .
HYDROGEOLOGY JOURNAL, 2015, 23 (06) :1109-1119
[48]   Solving the Multiobjective Multiple Traveling Salesmen Problem Using Membrane Algorithm [J].
He, Juanjuan .
BIO-INSPIRED COMPUTING - THEORIES AND APPLICATIONS, BIC-TA 2014, 2014, 472 :171-175
[49]   Optimal selection of harmonic filter branch parameters using PSO and differential evolution algorithm [J].
Parthasarathy, S. ;
Rajasekaran, V. ;
Gnanambal, K. .
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2014, 24 (10) :1434-1449
[50]   An Opposition-Based Self-adaptive Differential Evolution with Decomposition for Solving the Multiobjective Multiple Salesman Problem [J].
Chong, Jin Kiat ;
Qiu, Xin .
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, :4096-4103