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- 저자 : ( Hongyuan Gao ) (검색결과 4 건)
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Relay Selection Scheme Based on Quantum Differential Evolution Algorithm in Relay Networks
( Hongyuan Gao ),( Shibo Zhang ),( Yanan Du ),( Yuwang ),( Ming Diao ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.7
It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.
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Parameter Optimization Based on Evolutionary Algorithms for Green Cognitive Radio
Hongyuan Gao,Dandan Liu,Yanan Du 한국산학기술학회 2015 SmartCR Vol.5 No.5
In this paper, we study parameter adjustments that maximize the energy efficiency of green cognitive radio. Because the problem of parameter adjustments for green cognitive radio can be looked at as a complex discrete optimization problem, evolutionary algorithms can be applied to solve it. Parameter optimization methods based on quantum genetic algorithm (QGA), particle swarm optimization (PSO), and chaotic quantum particle swarm optimization (CQPSO) are designed. In particular, CQPSO integrates the characteristics of chaos with quantum particle swarm optimization (QPSO), which gives it strong global search abilities. Chaotic mutation is introduced into the proposed CQPSO to avoid premature convergence and keep diversity in populations. Quantum computing has excellent features used to increase optimization speed and enhance the search abilities of the algorithm. The proposed CQPSO method provides good performance in terms of convergence rate and convergence accuracy, and can search for an optimal solution to parameter adjustments in green cognitive radio networks. Through simulations comparing it to QGA and PSO, we conclude that the proposed CQPSO can improve energy efficiency and meet users’ quality of service needs.
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( Hongyuan Gao ),( Shihao Wang ),( Yumeng Su ),( Helin Sun ),( Zhiwei Zhang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7
In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.
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Hybrid Resource Allocation Scheme in Secure Intelligent Reflecting Surface-Assisted IoT
Su, Yumeng,Gao, Hongyuan,Zhang, Shibo Korean Society for Internet Information 2022 KSII Transactions on Internet and Information Syst Vol.16 No.10
With the rapid development of information and communications technology, the construction of efficient, reliable, and safe Internet of Things (IoT) is an inevitable trend in order to meet high-quality demands for the forthcoming 6G communications. In this paper, we study a secure intelligent reflecting surface (IRS)-assisted IoT system where malicious eavesdropper trying to sniff out the desired information from the transmission links between the IRS and legitimate IoT devices. We discuss the system overall performance and propose a hybrid resource allocation scheme for maximizing the secrecy capacity and secrecy energy efficiency. In order to achieve the trade-off between transmission reliability, communication security, and energy efficiency, we develop a quantum-inspired marine predator algorithm (QMPA) for realizing rational configuration of system resources and prevent from eavesdropping. Simulation results demonstrate the superiority of the QMPA over other strategies. It is also indicated that proper IRS deployment and power allocation are beneficial for the enhancement of system overall capacity.
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