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arXiv 2201.10281cs.ITmath.IT
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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

英文摘要:

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

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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