Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework

Salh, Adeeb and Ngah, Razali and Hussain, Ghasan Ali and Alhartomi, Mohammed and Boubkar, Salah and M. Shah, Nor Shahida and Alsulami, Ruwaybih and Alzahrani, Saeed (2023) Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework. Journal Pre-proof. pp. 1-10.

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Abstract

By considering the limited energy of Internet of Things (IoT) devices. We take the resource allocation to guarantee the stringent Quality of Service (QoS) depending on the joint optimization of power control and finite blocklength of channel. To achieve large volumes of arrival rates, we propose Adversarial Training based Generative Adversarial Networks (AT-GANs), which utilize a significant number of extreme events to provide high reliability and adjust real data in real-time. Simulation results show that Deep- Reinforcement Learning (Deep-RL) for AT-GAN could eliminate the transient training time. As a result, the AT-GAN keeps the reliability higher than 99.9999%.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things, Quality of Service, Generative Adversarial Networks, Deep Reinforcement Learning
Subjects:	T Technology > T Technology (General)
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	24 Jan 2025 09:26
Last Modified:	24 Jan 2025 09:26
URI:	http://eprints.uthm.edu.my/id/eprint/11761

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