基于移动边缘计算的车联网资源分配方案研究

针对复杂战场环境中一般云计算无法满足指挥中枢对特种车辆海量数据处理的延时问题,提出一种基于分层移动边缘计算的资源分配方案。方案对边缘服务器工作过程中任务卸载和资源分配进行优化,根据不同任务的延迟容忍度,以优化Q学习算法制定任务卸载优先级策略,采用Q学习算法设计资源分配流程。为了验证提出方案的有效性,仿真分析了优化算法的中断概率和延迟性能。仿真结果表明：该方案能有效为车联网提供低时延的移动服务。     

陆战场中的计算卸载和资源分配

移动边缘计算（MEC）作为5G时代的关键技术之一,通过计算卸载的方式提升任务执行质量。区别于其他研究,以陆战场为应用背景,针对多类型卸载任务共存的情况,考虑各类型任务的属性及需求,划分任务卸载优先级,以最小化系统开销为目标的同时提升高卸载优先级任务的卸载数量。通过仿真验证上述方法的可行性与优越性。     

蜂窝网络中基于干扰控制的D2D通信联合资源分配方案

在蜂窝网络中部署D2D(Device-to-Device)通信能够有效提升频谱利用率,降低基站负载,但D2D用户与蜂窝用户共享无线信道时会产生信号干扰。提出了一种联合资源分配算法,通过综合考虑信道分配对网络中已有的蜂窝用户和D2D用户的信号干扰,并在小区范围内寻找具有最小干扰值的信道资源分配给用户,以实现有效的干扰控制。仿真结果显示:联合资源分配能够提升D2D链路、蜂窝链路的信噪比及系统总吞吐量,使得蜂窝网络的整体性能优于独立资源分配。     

Optimal resource allocation and redistribution strategy in military conflicts with Lanchester square law attrition

We address the problem of optimal decision‐making in conflicts based on Lanchester square law attrition model where a defending force needs to be partitioned optimally, and allocated to two different attacking forces of differing strengths and capabilities. We consider a resource allocation scheme called the Time Zero Allocation with Redistribution (TZAR) strategy, where allocation is followed by redistribution of defending forces, on the occurrence of certain decisive events. Unlike previous work on Lanchester attrition model based tactical decision‐making, which propose time sequential tactics through an optimal control approach, the present article focuses on obtaining simpler resource allocation tactics based on a static optimization framework, and demonstrates that the results obtained are similar to those obtained by the more complex dynamic optimal control solution. Complete solution for this strategy is obtained for optimal partitioning of resources of the defending forces. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008