Deep Reinforcement Learning-Based Dynamic Scheduling for Real-Time Applications in LTE and RAN Slicing for eMBB in 5G

Deep Reinforcement Learning-Based Dynamic Scheduling for Real-Time Applications in LTE and RAN Slicing for eMBB in 5G

While LTE (4G) networks offer high-speed data transmission, 5G is necessary to meet the growing demands of modern communication. The main goals of 5G are to make communications more agile by significantly increasing network capacity, lowering latency, and speeding up the networks to handle a massive...

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Bibliographic Details
Main Authors: Houssem Eddine Benmadani, Mohamed Azni, Turki Essa Alharbi, Mohammed S. Alzaidi, Mohamed Tounsi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Packet scheduling algorithms
deep reinforcement learning
LTE
5G
network slicing
eMBB slices
Online Access:https://ieeexplore.ieee.org/document/10883949/
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https://ieeexplore.ieee.org/document/10883949/

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