低地球轨道(Low Earth Orbit,LEO)卫星系统因能够提供多媒体通信服务而成为卫星通信研究的热点.有效的路由算法设计是LEO卫星网络中的一个关键问题.为了满足多媒体应用的服务质量(Quality of Service,QoS)要求,卫星的路由算法应当考虑切换的影响.文章提出了一种基于启发式蚂蚁算法的分布式的QoS路由策略,可以满足延时限制,同时避免链路拥塞.仿真结果显示在不同的延时限制条件下,相对于最短路径优先算法,该算法具有较低的呼叫阻塞概率.
近年来提出的正交时频空(Orthogonal Time Frequency Space,OTFS)技术由于具有良好的多普勒频偏和时延适应性,在高动态通信场景下得到应用。目前该技术的信道状态信息(CSI)获取的主要方式,仍是传统的信道估计及其改进算法。对此,采用深度学习的方法估计CSI并直接恢复传输符号,使用基于抽头延迟线(Tapped Delay Line,TDL)信道模拟生成的数据离线训练深度学习模型,然后将该模型直接用于恢复在线传输的数据。仿真结果表明,在高频偏和多径效应下,基于深度学习的方法比传统方法更优,从而证明了在OTFS系统进行信道估计与信号检测中深度学习的前途。
Mobility management is one of the key problems in the mobile communication techniques. The performance of the mobile communication networks is heavily depended on the effectiveness of the mobility management. In this paper, we have presented a protocol of the mobility management in the mobile IP networks, based on the distributed RSs. When the mobile user in the inactive connection handoffs, some pointers are constructed and modified by the RSs and routers. It can realize the local registration; only when a call arrives, the possible update operations of the mobile database are needed. Consequently, the access of the RS is limited, and the cost of the mobility management is also reduced.
