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      A Fast Anti-jamming Decision Method Based on the Rule-Reduced Genetic Algorithm = A Fast Anti-jamming Decision Method Based on the Rule-Reduced Genetic Algorithm

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      https://www.riss.kr/link?id=A103334161

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      다국어 초록 (Multilingual Abstract)

      To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods.
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      To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision...

      To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods.

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      참고문헌 (Reference)

      1 Jin C, "Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm" 5 (5): 398-405, 2011

      2 Lei Xu, "Resource Allocation Algorithm based on Hybrid Particle Swarm Optimization for Multiuser Cognitive OFDM Network" 42 (42): 7186-7196, 2015

      3 Tzyy-Chyang Lu, "Quantum-Based Algorithm for Optimizing Artificial Neural Networks" 24 (24): 1266-1278, 2013

      4 Lei Xu, "Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network" 79 (79): 1591-1607, 2014

      5 T. R. Newman, "Population adaptation for genetic algorithm-based cognitive radios" 13 (13): 442-451, 2008

      6 T.R.Newman, "Multiple objective fitness for cognitive radio adaption" ProQuest 2008

      7 Ikno Kim, "DNA-Based Algorithm for Miniminzing Decision Rules:A Rough Sets Approach" 10 (10): 139-151, 2011

      8 Shengbo Yao, "Approach to stochastic multi-attribute decision problems using rough sets theory" 17 (17): 103-108, 2006

      9 Liang Xiao, "Anti-jamming Transmission Stackelberg Game With Observation Errors" 19 (19): 949-952, 2015

      10 Xiaoming Dai, "Allele Gene Based Adaptive Genetic Algorithm to the Code Design" 59 (59): 1253-1258, 2011

      1 Jin C, "Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm" 5 (5): 398-405, 2011

      2 Lei Xu, "Resource Allocation Algorithm based on Hybrid Particle Swarm Optimization for Multiuser Cognitive OFDM Network" 42 (42): 7186-7196, 2015

      3 Tzyy-Chyang Lu, "Quantum-Based Algorithm for Optimizing Artificial Neural Networks" 24 (24): 1266-1278, 2013

      4 Lei Xu, "Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network" 79 (79): 1591-1607, 2014

      5 T. R. Newman, "Population adaptation for genetic algorithm-based cognitive radios" 13 (13): 442-451, 2008

      6 T.R.Newman, "Multiple objective fitness for cognitive radio adaption" ProQuest 2008

      7 Ikno Kim, "DNA-Based Algorithm for Miniminzing Decision Rules:A Rough Sets Approach" 10 (10): 139-151, 2011

      8 Shengbo Yao, "Approach to stochastic multi-attribute decision problems using rough sets theory" 17 (17): 103-108, 2006

      9 Liang Xiao, "Anti-jamming Transmission Stackelberg Game With Observation Errors" 19 (19): 949-952, 2015

      10 Xiaoming Dai, "Allele Gene Based Adaptive Genetic Algorithm to the Code Design" 59 (59): 1253-1258, 2011

      11 Guangming Lv, "A simulated annealing-new genetic algorithm and its application, Electronics and Optoelectronics(ICEOE)" 3 (3): 29-31, 2011

      12 He A., "A Survey of Artificial Intelligence for Cognitive Radios" 59 (59): 1578-1592, 2010

      13 Ho S. L, "A Quantum-Based Particle Swarm Optimization Algorithm Applied to Inverse Problems" 49 (49): 2069-2072, 2013

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      학술지등록 한글명 : KSII Transactions on Internet and Information Systems
      외국어명 : KSII Transactions on Internet and Information Systems
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2013-10-01 평가 등재학술지 선정 (기타) KCI등재
      2011-01-01 평가 등재후보학술지 유지 (기타) KCI등재후보
      2009-01-01 평가 SCOPUS 등재 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.45 0.21 0.37
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.32 0.29 0.244 0.03
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