A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified sce...A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.展开更多
With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper ...Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.展开更多
随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通...随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通信、环境监测、智能交通等领域展现出巨大的潜力.然而,SAGIN具有异构结构、链路动态性高、资源分布广泛等特征,给网络的高效管理与优化带来巨大的挑战.近年来,人工智能(Artificial intelligence, AI)技术凭借强大的感知、学习与自主决策能力应用于通信网络,为SAGIN的智能演进提供了新契机.本文首先系统介绍SAGIN网络架构的基本组成与关键特征,并梳理当前主流AI技术在网络优化中的主要技术体系与适配优势,包括机器学习、图神经网络以及强化学习.其次,本文深入探讨了AI技术在SAGIN中智能资源管理、移动性管理与路由优化、空中平台路径规划、任务卸载与计算协同等典型场景中的应用与最新进展.最后,本文总结了AI技术应用在SAGIN网络中面临的挑战并展望了AI与SAGIN融合发展的未来方向.本文概述了AI技术在SAGIN网络中应用的优势与进展,旨在为AI赋能的SAGIN研究与应用发展提供技术参考.展开更多
多功能雷达(Multi Function Radar,MFR)通过波形捷变与波束自适应调度实现多任务协同,这给雷达工作模式识别带来了诸多挑战。现有识别方法依赖脉冲序列局部时域特征,难以有效解析不同工作模式的生成机理,面对脉冲丢失、脉内参数相近等...多功能雷达(Multi Function Radar,MFR)通过波形捷变与波束自适应调度实现多任务协同,这给雷达工作模式识别带来了诸多挑战。现有识别方法依赖脉冲序列局部时域特征,难以有效解析不同工作模式的生成机理,面对脉冲丢失、脉内参数相近等复杂情况,识别性能急剧下降。考虑到多功能雷达波束扫描过程对脉冲组序列幅值信息的影响,提出一种基于空时联合图卷积网络的多功能雷达工作模式识别方法。该网络模型首先通过引入动态规整模块量化相邻波位信号的辐射特性相似度,构造具有物理可解释性的空域邻接矩阵;然后将一维脉冲组序列映射为二维图结构,融合脉冲频率、信号幅度等节点特征,形成空时联合表征;最后,设计分层图卷积核,通过多层信息传递机制,提取深层空时特征,完成雷达工作模式识别。对比实验表明,在脉冲丢失等非理想情况下所提方法的平均识别率仍能达到93.38%,具有更好的泛化性和鲁棒性。展开更多
基金supported by the Strategic Leading Project of the Chinese Academy of Sciences(No.XDA15014603).
文摘A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
文摘With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
基金Supported by the National Natural Science Foundation of China (No. 60972061,60972062,and 61032004)the National High Technology Research and Development Program of China ("863" Program) (No. 2008AA12A204)
文摘Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.
文摘多功能雷达(Multi Function Radar,MFR)通过波形捷变与波束自适应调度实现多任务协同,这给雷达工作模式识别带来了诸多挑战。现有识别方法依赖脉冲序列局部时域特征,难以有效解析不同工作模式的生成机理,面对脉冲丢失、脉内参数相近等复杂情况,识别性能急剧下降。考虑到多功能雷达波束扫描过程对脉冲组序列幅值信息的影响,提出一种基于空时联合图卷积网络的多功能雷达工作模式识别方法。该网络模型首先通过引入动态规整模块量化相邻波位信号的辐射特性相似度,构造具有物理可解释性的空域邻接矩阵;然后将一维脉冲组序列映射为二维图结构,融合脉冲频率、信号幅度等节点特征,形成空时联合表征;最后,设计分层图卷积核,通过多层信息传递机制,提取深层空时特征,完成雷达工作模式识别。对比实验表明,在脉冲丢失等非理想情况下所提方法的平均识别率仍能达到93.38%,具有更好的泛化性和鲁棒性。
