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Resource Management in LADNs Supporting 5G V2X Communications

机译:LADNS支持5G V2X通信的资源管理

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Local access data network (LADN) is a promising paradigm to reduce latency, enable lowering energy consumption, and improve quality of service (QoS) for the Fifth Generation (5G) radio access network (RAN) supporting vehicle to everything (V2X) communications. To achieve optimum resource allocation and save energy by minimizing the activation of LADN servers in Cloud-RAN, some remote radio heads (RRHs) can be turned on or off depending on the traffic demand. In this paper, we investigate the problem of how to realize effective resource management in 5G RAN supporting V2X communications. More precisely, we first propose a formulation of the resource management problem as an optimization problem with the objective of minimizing the number of RRHs to be turned on subject to the uplink bandwidth constraints. We then use a fully-fledged professional software to solve our optimization problem and propose a solution with heuristic algorithms to deal with the complexity of the problem for large scenarios. Moreover, we analyze the impact of the density of vehicles on the computation time and the influence of the uplink data rate and vehicle densities on the number of active RRHs. Our numerical results show that our proposed model can efficiently utilize the resources and provide optimum vehicles-to-RRHs associations which lead to energy-savings. For instance, to serve 100 vehicles with aggregated uplink data rate equal to 100 [Mbps], the optimal associations save about 70% of the energy comparing to the strongest-signal associations. Furthermore, we obtain optimal results for the small size problem in reasonable computation times, which are around 50 [ms].
机译:本地访问数据网络(LADN)是一个有前途的范例来减少延迟,使能耗降低,并提高第五代(5G)无线电接入网络(RAN)支持车辆的服务质量(QoS),支持车辆到所有(V2X)通信。为了通过最小化云运行中的Ladn服务器激活来实现最佳的资源分配和节省能量,可以根据流量需求打开或关闭一些远程无线电头(RRH)。在本文中,我们调查了如何实现5G RAN支持V2X通信的有效资源管理的问题。更确切地说,我们首先提出了资源管理问题的制定作为优化问题,目的是最小化对上行链路带宽约束的RRH的数量。然后,我们使用完全成熟的专业软件来解决我们的优化问题,并提出一种具有启发式算法的解决方案来处理大型场景问题的复杂性。此外,我们分析了车辆密度对计算时间的影响以及上行链路数据速率和车辆密度对有源RRH的数量的影响。我们的数值结果表明,我们的拟议模型可以有效地利用资源,并为RRHS关联提供最佳的车辆,这导致节能。例如,为了服务100辆具有等于100 [Mbps]的聚合上行链路数据速率的车辆,最佳关联可保存与最强信号关联的约70%的能量。此外,我们在合理的计算时间内获得了小尺寸问题的最佳结果,这是大约50 [MS]。

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