Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning

2022/01/25 by López-Sánchez, M., Villena-Rodríguez, A., Gómez, G. +2
#FOS: Computer and information sciences #Information Theory (cs.IT)

paper · doi:10.48550/arxiv.2201.10281

Abstract

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

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