Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning

In this paper, we propose a novel deep reinforcement learning (DRL) framework to maximize user fairness in terms of delay. To this end, we devise a new version of the modified largest weighted delay first (M-LWDF) algorithm, called
$\beta$
-M-LWDF, aiming to fulfill an appropriate balance between user fairness and average delay. This balance is defined as a feasible region on the cumulative distribution function (CDF) of the user delay that allows identifying
unfair
states,
feasible-fair
states, and
over-fair
states. Simulation results reveal that our proposed framework outperforms traditional resource allocation techniques in terms of latency fairness and average delay.