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.

翻译：道路上的车辆通过车对基础设施(V2I)通信频繁与基站(BS)交换数据，以确保车辆应用的正常使用，其中采用IEEE 802.11分布式协调功能(DCF)来分配用于信道接入的最小竞争窗口(MCW)。每辆车可能更改其MCW以便以其他车辆为代价获得更多接入机会，从而导致通信性能不公平。此外，关键接入参数MCW属于隐私信息，各车辆不愿与其他车辆共享。在这种不确定环境下，信息年龄(AoI)是衡量数据新鲜度的重要通信指标，我们设计了一种智能车载节点，用于学习动态环境并预测可实现年龄公平的最优MCW。为了为车载节点分配最优MCW，我们采用一种学习算法，通过从回放历史数据中学习来做出理想决策。具体而言，该算法通过扩展传统DQN训练与测试方法而提出。最后，通过与其他方法对比，证明所提出的DQN方法能够显著提升智能节点的年龄公平性。