Towards V2I Age-Aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method

Abstract

Vehicles on the road exchange data with base station frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function 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 parameter MCW is privacy information and each vehicle is not willing to share it with other vehicles. In this uncertain setting, age of information (AoI), which measures the freshness of data and is closely related with fairness, has become an important communication metric. On this basis, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW, which can make the intelligent node 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 deep-Q-learning (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.