  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.
