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

Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning López-Sánchez, María Villena-Rodríguez, Alejandro Gómez-Paredes, Gerardo Martín-Vega, Francisco-Javier Aguayo-Torres, María del Carmen Aprendizaje automático (Inteligencia artificial) Sistemas de comunicación inalámbricos 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. 2025-11-21T12:49:35Z 2025-11-21T12:49:35Z 2025-11-21T12:49:35Z 2022-11-23 journal article 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 eng info:eu-repo/grantAgreement/JuntaAndalucia/PAIDI/P18-RT-3175/// info:eu-repo/grantAgreement/JuntaAndalucia/PAIDI/P18-TP-3587/// info:eu-repo/grantAgreement/JuntaAndalucia/PAIDI/UMA20-FEDERJA-002/// info:eu-repo/grantAgreement/JuntaAndalucia/PAIDI2020/DOC_00265/// open access IEEE