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oai:riuma.uma.es:10630/408722026-02-03T10:52:24Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc López-Sánchez, María author Villena-Rodríguez, Alejandro author Gómez-Paredes, Gerardo author Martín-Vega, Francisco-Javier author Aguayo-Torres, María del Carmen author 2022-11-23 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 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. M. López-Sánchez, A. Villena-Rodríguez, G. Gómez, F. J. Martín-Vega and M. C. Aguayo-Torres, "Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning," in IEEE Transactions on Vehicular Technology, vol. 72, no. 4, pp. 5407-5412, April 2023, doi: 10.1109/TVT.2022.3224368 https://hdl.handle.net/10630/40872 10.1109/TVT.2022.3224368 Aprendizaje automático (Inteligencia artificial) Sistemas de comunicación inalámbricos Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning