论文标题
迈向V2I年龄意识公平访问：基于DQN的智能车辆节点培训和测试方法
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
论文作者
论文摘要
与基础设施（V2I）通信经常与基站（BS）的道路交换数据（BS）上的车辆，以确保使用IEEE 802.11分布式协调功能（DCF）的正常使用车辆应用，以分配最小的竞争窗口（MCW）以访问渠道。每辆车可能会更改其MCW，以获得更多的访问机会，以牺牲他人为代价，从而导致不公平的沟通绩效。此外，关键访问参数MCW是隐私信息，每辆车都不愿意与其他车辆共享。在这种不确定的环境中，信息时代（AOI）是衡量数据新鲜度的重要通信指标，我们设计了一个智能的车辆节点来学习动态环境并预测最佳的MCW，这可以使其达到年龄公平。为了为车辆节点分配最佳MCW，我们使用学习算法来通过从重播历史数据中学习来做出理想的决定。特别是，通过扩展传统的DQN培训和测试方法提出该算法。最后，通过与其他方法进行比较，可以证明所提出的DQN方法可以显着改善智能节点的年龄公平性。
Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function (DCF) is employed to allocate a minimum contention window (MCW) for channel access. Each vehicle may change its MCW to achieve more access opportunities at the expense of others, which results in unfair communication performance. Moreover, the key access parameters MCW is the privacy information and each vehicle are not willing to share it with other vehicles. In this uncertain setting, age of information (AoI) is an important communication metric to measure the freshness of data, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW which can make it achieve age fairness. In order to allocate the optimal MCW for the vehicular node, we employ a learning algorithm to make a desirable decision by learning from replay history data. In particular, the algorithm is proposed by extending the traditional DQN training and testing method. Finally, by comparing with other methods, it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node.
