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.
翻译:在本文中,我们考虑了高速铁路(HSR)情况下的以毫米为瓦瓦为基础的火车地面通信系统,其中用户的计算任务可部分卸至铁路边基站或火车顶部部署的移动继电器(MRs),MRs以全双轨模式运作,以实现高频谱利用。我们提出了在本地装置和MRs能源消耗限制下最大限度地减少所有用户的平均任务执行延迟度的问题。我们建议了联合资源分配和计算卸载计划(JRACO)以解决问题。它包括资源分配和计算卸载(RACO)算法和MR能源约束算法。RACO利用匹配游戏理论在两个子方案(即数据分割和用户关联以及分流分配)之间进行迭代。根据RACO的结果,MR能源限制算法确保满足MR的能源消耗限制。广泛的模拟证实,JRACO可以有效地减少平均延迟度并增加服务用户人数,而与三个基线方案相比。