RT Journal Article
T1 Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning
A1 López-Sánchez, María
A1 Villena-Rodríguez, Alejandro
A1 Gómez-Paredes, Gerardo
A1 Martín-Vega, Francisco-Javier
A1 Aguayo-Torres, María del Carmen
K1 Aprendizaje automático (Inteligencia artificial)
K1 Sistemas de comunicación inalámbricos
AB 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.
PB IEEE
YR 2022
FD 2022-11-23
LK https://hdl.handle.net/10630/40872
UL https://hdl.handle.net/10630/40872
LA eng
NO 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
NO https://openpolicyfinder.jisc.ac.uk/id/publication/3582
NO Unión Europea
NO Junta de Andalucía
NO Universidad de Málaga
NO Agencia Estatal de Investigación
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026