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：与基地站(BS)的车辆交换数据时,通常通过车辆与基站(V2I)通信的车辆交换数据,以确保正常使用车辆应用(V2I)通信,其中IEEE 802.11的分布式协调功能(DCF)被用来分配一个最小的争议窗口(MCW),供频道使用,每一车辆都可能改变其MCW,以牺牲他人的利益为代价获得更多的接入机会,从而造成不公平的通信性能。此外,MCW的关键访问参数是隐私信息,而每辆车不愿意与其他车辆分享这些信息。在这一不确定的环境下,信息年龄(AoI)是衡量数据新鲜度的重要通信指标,我们设计了一个智能的语音节点,以学习动态环境,预测最佳的 MCW能够实现年龄公平性。为将最佳的 MCW分配给其他车辆,从而造成不公平的通信性能。我们采用学习算法,通过从历史数据中学习来做出可取的决定。特别是,通过扩大传统的DQN培训和测试方法提出算法。最后,通过比较其他方法,证明拟议的DQN的智能方法能够大大提高年龄的公平性。</s>