학술논문
Latency Fairness Optimization on Wireless Networks Through Deep Reinforcement Learning
Document Type
Periodical
Source
IEEE Transactions on Vehicular Technology IEEE Trans. Veh. Technol. Vehicular Technology, IEEE Transactions on. 72(4):5407-5412 Apr, 2023
Subject
Language
ISSN
0018-9545
1939-9359
1939-9359
Abstract
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.