High frequency（HF）transmission is an important communication techniques.However,conven-tional point-to-point transmission can be easily destroyed,which limits its utilization in practice.HF networking communication ...High frequency（HF）transmission is an important communication techniques.However,conven-tional point-to-point transmission can be easily destroyed,which limits its utilization in practice.HF networking communication has the capability against demolishment.The network structure is one of the key factors for HF networking communication.In this paper,a novel analysis method of the network connectedness based on the eigenvalue is derived,and a multi-layer distributed HF radio network structure is proposed.Both the theore-tical analysis and the computer simulation results verify that the application of the proposed network structure in the HF radio communication can improve the anti-demolishment ability of the HF network efficiently.展开更多
High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and...High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.展开更多
优化链路状态路由(Optimized Link State Routing,OLSR)协议是一种先验式路由协议,网络中的所有节点通过周期性地发送控制消息来计算全网路由信息。在短波自组织网络中,节点周期性地发送控制消息会占据大量的信道资源,大幅增加网络的控...优化链路状态路由(Optimized Link State Routing,OLSR)协议是一种先验式路由协议,网络中的所有节点通过周期性地发送控制消息来计算全网路由信息。在短波自组织网络中,节点周期性地发送控制消息会占据大量的信道资源,大幅增加网络的控制开销,浪费短波有限的带宽资源,导致网络通信性能急剧下降。其次,受到地形地貌、天线方向和接收性能的个体差异等影响,造成无线链路不稳定,导致网络中存在非对称链路,增加了通信端到端时延。为此,提出了一种低时延的短波自组网OLSR协议。该协议在执行MPR(Multipoint Relay)选择算法时综合考虑了节点的连接度和链路可靠性,在优化MPR节点个数的同时选择链路可靠性较大的节点作为MPR节点,在进行路由选择时能够利用网络中的非对称链路。仿真结果表明,该协议能优化数据包投递成功率、吞吐量、端到端时延和网络控制开销等性能指标。展开更多
基金supported by the Key Laboratory Project of National Defense Fund under Grant No. 51434090104JB0202the Advance Research Project of National Defense Science and Technology Fund under Grant No. 51406020205JB0204
文摘High frequency（HF）transmission is an important communication techniques.However,conven-tional point-to-point transmission can be easily destroyed,which limits its utilization in practice.HF networking communication has the capability against demolishment.The network structure is one of the key factors for HF networking communication.In this paper,a novel analysis method of the network connectedness based on the eigenvalue is derived,and a multi-layer distributed HF radio network structure is proposed.Both the theore-tical analysis and the computer simulation results verify that the application of the proposed network structure in the HF radio communication can improve the anti-demolishment ability of the HF network efficiently.
基金supported in part by the National Natural Science Foundation of China (Grants No. 61501510 and No. 61631020)Natural Science Foundation of Jiangsu Province (Grant No. BK20150717)+2 种基金China Postdoctoral Science Foundation Funded Project (Grant No. 2016M590398 and No.2018T110426)Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1501009A)Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (Grant No. BK20160034)
文摘High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.
文摘优化链路状态路由(Optimized Link State Routing,OLSR)协议是一种先验式路由协议,网络中的所有节点通过周期性地发送控制消息来计算全网路由信息。在短波自组织网络中,节点周期性地发送控制消息会占据大量的信道资源,大幅增加网络的控制开销,浪费短波有限的带宽资源,导致网络通信性能急剧下降。其次,受到地形地貌、天线方向和接收性能的个体差异等影响,造成无线链路不稳定,导致网络中存在非对称链路,增加了通信端到端时延。为此,提出了一种低时延的短波自组网OLSR协议。该协议在执行MPR(Multipoint Relay)选择算法时综合考虑了节点的连接度和链路可靠性,在优化MPR节点个数的同时选择链路可靠性较大的节点作为MPR节点,在进行路由选择时能够利用网络中的非对称链路。仿真结果表明,该协议能优化数据包投递成功率、吞吐量、端到端时延和网络控制开销等性能指标。
