Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning
M. López-Sánchez, Alejandro Villena-Rodríguez, Gerardo Gómez, Francisco J. Martín-Vega, Mari Carmen Aguayo‐Torres · IEEE Transactions on Vehicular Technology · 2022
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 identifyingunfairstates,feasible-fairstates, andover-fairstates. Simulation results reveal that our proposed framework outperforms traditional resource allocation techniques in terms of latency fairness and average delay.