Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning

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 \beta-M-LWDF, aiming to fulfill an appropriate balance between user...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2023-04, Vol.72 (4), p.5407-5412
Main Authors: Lopez-Sanchez, Maria, Villena-Rodriguez, Alejandro, Gomez, Gerardo, Martin-Vega, Francisco J., Aguayo-Torres, Mari Carmen
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Delay
Delays
Distribution functions
latency
Network latency
Optimization
Quality of service
reinforcement learning
Resource allocation
Scheduling
Signal processing algorithms
Signal to noise ratio
System performance
Throughput
Wireless networks
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Summary: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 \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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3224368