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arXiv · 2201.10281

Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning

M. López-Sánchez·A. Villena-Rodríguez·G. Gómez·F. J. Martín-Vega·M. C. Aguayo-Torres
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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 $\beta$-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

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M. López-Sánchez, A. Villena-Rodríguez, G. Gómez, F. J. Martín-Vega, M. C. Aguayo-Torres. 2022-01-25. Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning. https://arxiv.org/abs/2201.10281

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