基于Hopfield网络的短波协同侦收设备调度技术
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摘要
短波波段信号密集,电磁环境复杂,相对有限侦收设备,往往会导致侦收效率不高。针对这种情况,提出了基于Hopfield神经网络的短波侦收设备调度方法,对短波信号进行协同侦收。给出了有效侦收概率的定义,梳理了有效侦收的约束条件,设计了侦收设备调度的数学模型,通过Hopfield神经网络实现了此模型。该方法利用全局优化技术对多个侦收传感器进行调度,使不同侦收传感器合理地协同侦收,实现侦收系统整体效益最大化。仿真结果表明了该方法的可行性和有效性。
Because of the many and dense signals in High Frequency(HF) band,the complex electromagnetic environment and the relatively limited resources,the efficiency of HF reception is not high. In view of this situation,a resource scheduling method for HF reception based on Hopfield neural network is proposed to reach cooperative HF reception. The effective reception probability is defined,the constraints of reception are given,a mathematical model of reception device scheduling is designed and realized by Hopfield neural networks. To achieve the optimum performance of the reception system,the optimization techniques of global scheduling for multiple sensor reception are utilized to make the cooperative reception reasonably. The experimental results show the effectiveness of the proposed method.
Because of the many and dense signals in High Frequency(HF) band,the complex electromagnetic environment and the relatively limited resources,the efficiency of HF reception is not high. In view of this situation,a resource scheduling method for HF reception based on Hopfield neural network is proposed to reach cooperative HF reception. The effective reception probability is defined,the constraints of reception are given,a mathematical model of reception device scheduling is designed and realized by Hopfield neural networks. To achieve the optimum performance of the reception system,the optimization techniques of global scheduling for multiple sensor reception are utilized to make the cooperative reception reasonably. The experimental results show the effectiveness of the proposed method.
引文
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[2]PAN J Y,Chang K W,LIN H W,et al.Uplink shared resource scheduling for device-to-device underlay communication with least transmission power resource selection and guartanteed link quality[C]//2016 International Conference on Networking and Network Applications,2016:364-369.
[3]LI Y,HAN J Z,ZHOU W.Cress:dynamic scheduling for resource constrained jobs[C]//2014 IEEE 17th International Conference on Computational Science and Engineering,2014:1945-1953.
[4]CHEN N S,FANG X P,WANG X.A cloud computing resource scheduling scheme based on estimation of distribution algorithm[C]//2014 2nd International Conference on System and Informatics,2014:304-308.
[5]CHEN Z G,ZHAN Z H,LI H H,et al.Deadline constrained cloud computing resources scheduling through an ant colony system approach[C]//2015 International Conference on Cloud Computing Research and Innovation,2015:112-119.
[6]LI H H,FU Y W,ZHAN Z H,et al.Renumber strategy enhanced particle swarm optimization for cloud computing resource[C]//2015 IEEE Congress on Evolutionary Computation,2015:870-876.
[7]FEI C J,ZHAO B K,WU C Q,et al.Towards efficient radio resource scheduling in LTE-based satellite mobile communication system[C]//The 2015 IEEE INFOCOM international Workshop on Mobility Management in the Networks of the Future World,2015:167-172.
[8]LI M D,THAM C K.Resource scheduling in SC-FDMA based relay wireless sensor networks[J].Ad-hoc and Sensor Networking Symposium,2014:397-402.
[9]RONG H G,LI J F,CHANG B G,et al.A crowd sourcing service model for optimizing user-desired storage resource scheduling[C]//2015 IEEE 17th International Conference on High Performance Computing and Communications,2015.
[10]YOU C H,ZHANG Y.A radio resource scheduling scheme for periodic M2M communications in celluar networks[C]//2014 6th International Conference on Wireless Communications and Signal Processing,2014:978-983.
[11]郝才勇,彭忠宝.短波监测覆盖范围分析[J].中国无线电,2015,30(12):56-58.
[12]张昀,于舒娟,刘欢.改进的无线传感网混沌Hopfield盲检测算法[J].电子技术应用,2014,40(11):95-98.
[13]曹金金.OFDM无线多跳网络中基于Hopfield神经网络的资源分配算法[D].西安:西安电子科技大学,2014.
[14]文仁强,钟少波,袁宏永,等.应急资源多目标优化调度模型与多蚁群优化算法研究[J].计算机研究与发展,2013,50(7):1464-1472.