论文标题

资源分配和计算在毫米波火车地面网络中卸载

Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network

论文作者

Li, Linqian, Niu, Yong, Mao, Shiwen, Ai, Bo, Zhong, Zhangdui, Wang, Ning, Chen, Yali

论文摘要

在本文中,我们考虑了在高速铁路(HSR)方案中基于MMWave的TraingRound通信系统,其中可以将用户的计算任务部分卸载到火车屋顶上的铁路侧基站(BS)或移动继电器(MRS)。 MRS以全双工(FD)模式运行,以实现高光谱利用率。我们制定了在本地设备和MRS能源消耗限制下,将所有用户的平均任务执行延迟最小化的问题。我们建议联合资源分配和计算卸载方案(JRACO)来解决问题。它由资源分配和计算卸载(RACO)算法和MR能量约束算法组成。 RACO利用匹配的游戏理论在两个子问题之间进行迭代,即数据分割,用户关联以及子渠​​道分配。通过RACO结果,MR能量限制算法确保满足MR能耗约束。广泛的模拟验证了与三个基线方案相比,JRACO可以有效地减少平均潜伏期并增加服务用户的数量。

In this paper, we consider an mmWave-based trainground communication system in the high-speed railway (HSR) scenario, where the computation tasks of users can be partially offloaded to the rail-side base station (BS) or the mobile relays (MRs) deployed on the roof of the train. The MRs operate in the full-duplex (FD) mode to achieve high spectrum utilization. We formulate the problem of minimizing the average task execution latency of all users, under local device and MRs energy consumption constraints. We propose a joint resource allocation and computation offloading scheme (JRACO) to solve the problem. It consists of a resource allocation and computation offloading (RACO) algorithm and an MR Energy constraint algorithm. RACO utilizes the matching game theory to iterate between two subproblems, i.e., data segmentation and user association and sub-channel allocation. With the RACO results, the MR energy constraint algorithm ensures that the MR energy consumption constraint is satisfied. Extensive simulations validate that JRACO can effectively reduce the average latency and increase the number of served users compared with three baseline schemes.

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