User Plane Function (UPF) Allocation for C-V2X Network Using Deep Reinforcement Learning

In this paper, we proposed an online learning method for predicting an allocation of User Plane Function (UPF) in Cellular Vehicle-to-Everything (C-V2X) networks integrated with Multi-Access Edge Computing (MEC). Our study employed Deep Reinforcement Learning (DRL) techniques, specifically Deep Q-Ne...

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Veröffentlicht in:	IEEE access 2025, Vol.13, p.4547-4561
Hauptverfasser:	Sasithong, Pruk, Sanguanpuak, Tachporn, Vanichchanunt, Pisit, Wuttisittikulkij, Lunchakorn
Format:	Artikel
Sprache:	eng
Schlagworte:	5G mobile communication 5G network cellular vehicle-to-everything (C-V2X) Cloud computing Deep learning deep reinforcement learning (DRL) Dynamic scheduling Edge computing Greedy algorithms Mobile computing Multi-access edge computing multi-access edge computing (MEC) Network latency Real-time systems Reliability Resource management Throughput Vehicle dynamics Vehicle-to-everything
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