论文标题
车辆网络切片，可靠访问和截止日期限制的数据卸载：一种多机构学习方法
Vehicular Network Slicing for Reliable Access and Deadline-Constrained Data Offloading: A Multi-Agent On-Device Learning Approach
论文作者
论文摘要
有效的数据卸载在计算密集型平台中起关键作用，因为无线通道的数据速率在根本上受到限制。最重要的是，高机动性增加了车辆边缘网络（VEN）的额外负担，增强了对有效以用户为中心的解决方案的渴望。因此，与旧式稳定的以网络为中心的方法不同，本文利用了软件定义的灵活，开放和可编程的网络平台，以在Vens中有效地以用户为中心，快速，可靠，可靠和截止日期约束的卸载解决方案。在拟议的模型中，通过创建一个高贵的虚拟单元（VC），从多个低功率的接入点（AP）提供每个活动的车辆用户（VU）。提出了联合节点关联，功率分配和分布式资源分配问题。由于在许多现实世界中，遵循自主VUS的分布性质，集中学习是不可实际的，因此每个VU被认为是边缘学习推动者。为此，考虑到实用的位置感知节点关联，制定了联合无线电资源分配非合作随机游戏。提出了利用强化学习（RL）功效，提出了一个多代理RL（MARL）解决方案，其中边缘学习者旨在学习NASH平衡（NE）策略以有效地解决游戏。此外，使用实用微观迁移率模型的现实世界地图数据用于模拟。结果表明，提出的以用户为中心的方法可以在Vens中提供出色的性能。此外，在上行链路中分布式随机访问的情况下，提出的MALL解决方案可提供近乎最佳的性能，约3％的碰撞概率。
Efficient data offloading plays a pivotal role in computational-intensive platforms as data rate over wireless channels is fundamentally limited. On top of that, high mobility adds an extra burden in vehicular edge networks (VENs), bolstering the desire for efficient user-centric solutions. Therefore, unlike the legacy inflexible network-centric approach, this paper exploits a software-defined flexible, open, and programmable networking platform for an efficient user-centric, fast, reliable, and deadline-constrained offloading solution in VENs. In the proposed model, each active vehicle user (VU) is served from multiple low-powered access points (APs) by creating a noble virtual cell (VC). A joint node association, power allocation, and distributed resource allocation problem is formulated. As centralized learning is not practical in many real-world problems, following the distributed nature of autonomous VUs, each VU is considered an edge learning agent. To that end, considering practical location-aware node associations, a joint radio and power resource allocation non-cooperative stochastic game is formulated. Leveraging reinforcement learning's (RL) efficacy, a multi-agent RL (MARL) solution is proposed where the edge learners aim to learn the Nash equilibrium (NE) strategies to solve the game efficiently. Besides, real-world map data, with a practical microscopic mobility model, are used for the simulation. Results suggest that the proposed user-centric approach can deliver remarkable performances in VENs. Moreover, the proposed MARL solution delivers near-optimal performances with approximately 3% collision probabilities in case of distributed random access in the uplink.
