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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Published in:IEEE access Vol. 13; pp. 33555 - 33570
Main Authors: Eddine Benmadani, Houssem, Azni, Mohamed, Essa Alharbi, Turki, Alzaidi, Mohammed S., Tounsi, Mohamed
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
5G mobile communication
Algorithms
Broadband
Data transmission
Deep learning
deep reinforcement learning
Dynamic scheduling
eMBB slices
Heuristic algorithms
Long Term Evolution
LTE
Machine learning
Mobile computing
Network latency
Network slicing
OFDM
Optimal scheduling
Packet scheduling algorithms
Quality of service
Quality of service architectures
Real time
real-time applications
Real-time systems
Resource allocation
Resource management
Scheduling
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ISSN:2169-3536, 2169-3536
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Summary: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 number of connected devices. To materialize this vision, a new concept has emerged in 5G called network slicing; hence, network providers can offer tailored quality of service based on every type of user according to their specific requirements. However, efficient resource allocation in LTE and across slices or even within a slice in 5G systems to meet QoS is still one of the most critical issues for network designers while resources change over the complexity, dynamic nature, and resource constraints of the network. Classic resource allocation methods cannot cope with the dynamics of the changing network environment; hence, there is an increasing need for faster and smarter resource allocation schemes. This paper proposes an efficient dynamic scheduling algorithm based on machine learning methods for real-time applications in LTE networks, extending to 5G using Radio Access Network (RAN) slicing with a focus on enhanced Mobile Broadband (eMBB) slices. Carrying out extensive simulations and evaluations by using the ns-3 simulator, we compare and analyze various downlink scheduling rules in terms of system throughput, spectral efficiency, average delay, and packet loss ratio. The simulation results show that the proposed algorithm always outperforms the QoS performance, especially for real-time applications.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3541531