面向低地球轨道(low earth orbit,LEO)卫星服务性能提升,针对LEO导航信号波形研究现状进行了综述,对比分析了基于线性调频(linear frequency modulation,LFM)的LEO导航信号结构,研究其多址实现和参数估计方法.总结LEO导航信号波形设计方...面向低地球轨道(low earth orbit,LEO)卫星服务性能提升,针对LEO导航信号波形研究现状进行了综述,对比分析了基于线性调频(linear frequency modulation,LFM)的LEO导航信号结构,研究其多址实现和参数估计方法.总结LEO导航信号波形设计方向,得知利用伪随机扩频码调制的LFM复合信号是一种较优的信号结合方式,未来可在此基础上进一步加强多址性能及改进参数估计方法.展开更多
With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have i...With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.展开更多
文摘面向低地球轨道(low earth orbit,LEO)卫星服务性能提升,针对LEO导航信号波形研究现状进行了综述,对比分析了基于线性调频(linear frequency modulation,LFM)的LEO导航信号结构,研究其多址实现和参数估计方法.总结LEO导航信号波形设计方向,得知利用伪随机扩频码调制的LFM复合信号是一种较优的信号结合方式,未来可在此基础上进一步加强多址性能及改进参数估计方法.
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806104in part by Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program+1 种基金in part by Natural Sciences and Engineering Research Council of Canada (NSERC)the support from Huawei
文摘With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.
