With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

中文翻译：

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基于联合学习的轨迹预测，用于移动小蜂窝网络中的动态资源分配

随着第五代（5G）移动通信的演进，车辆边缘计算（VEC）和移动小蜂窝（MSC）网络因其能够为车辆用户提供改进的服务质量（QoS）而受到关注。支持VEC的MSC网络的最终目标是减少车辆渗透效应和路径损耗，从而提高车辆用户的网络性能。在本文中，我们探索了支持 VEC 的 MSC 网络中具有概率移动性的 MSC 前传链路的分布式资源块 (RB) 分配。所提出的工作利用路边单元（RSU）的计算能力，路边单元（RSU）部署有路边的VEC服务器，通过交换有限的信息以分布式方式将资源分配给MSC，目的是最大化MSC网络实现的数据速率。此外，我们提出基于联邦学习（FL）的MSC位置预测来提前预测MSC的轨迹，以有效预测MSC网络中的资源分配。给出了仿真结果，以在 MSC 网络的 RB 利用率和平均可实现数据速率方面比较位置预测相关的分布式和集中式时间间隔相关的基于干扰图的资源分配给 MSC。为了进行比较，我们进一步研究了集中式以及分布式阈值时间相关的基于干扰图的 MSC 网络资源分配。