In this paper, we propose a novel deep reinforcement learning 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, which is called β-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
@article{arxiv.2201.10281,
title = {Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning},
author = {M. López-Sánchez and A. Villena-Rodríguez and G. Gómez and F. J. Martín-Vega and M. C. Aguayo-Torres},
journal= {arXiv preprint arXiv:2201.10281},
year = {2022}
}