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

TL;DR

This paper proposes a DQN-based method for intelligent vehicular nodes to dynamically learn and predict optimal access parameters, improving age fairness in V2I communications without sharing private information.

Contribution

It introduces an age-aware fairness approach using an extended DQN algorithm to optimize contention window settings in V2I networks.

Findings

Significantly improves age fairness compared to existing methods.

Effectively predicts optimal contention windows without sharing private data.

Enhances data freshness in vehicular communications.

Abstract

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.