Bandwidth allocation of URLLC for real-time packet traffic in B5G: A Deep-RL framework
Adeeb Salh,
Razali Ngah,
Ghasan Ali Hussain,
Mohammed Alhartomi,
Salah Boubkar,
Nor Shahida M. Shah,
Ruwaybih Alsulami,
Saeed Alzahrani
Affiliations
Adeeb Salh
Faculty of Information and Communication Technology, University Tunku Abdul Rahman (UTAR), Kampar 31900, Malaysia
Razali Ngah
Wireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia; Corresponding author.
Ghasan Ali Hussain
Department of Electrical Engineering, Faculty of Engineering. University of Kufa, Iraq
Mohammed Alhartomi
Department of Electrical Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
Salah Boubkar
Wireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia; Higher Institute of Science and Technology - Albrkat - Ghat, Libya
Nor Shahida M. Shah
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
Ruwaybih Alsulami
Department of Electrical Engineering, Umm Al Qura University, Makkah, Saudi Arabia
Saeed Alzahrani
Department of Electrical Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
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%.
