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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Veröffentlicht in:	IEEE access Jg. 13; S. 33555 - 33570
Hauptverfasser:	Eddine Benmadani, Houssem, Azni, Mohamed, Essa Alharbi, Turki, Alzaidi, Mohammed S., Tounsi, Mohamed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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 Symbols
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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