Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework
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 Adv...
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| Format: | Article |
| Language: | en |
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ScienceDirect
2023
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| Online Access: | http://eprints.uthm.edu.my/11761/1/J17170_8220a34f4585fd4860d50eb6631fae63.pdf http://eprints.uthm.edu.my/11761/ https://doi.org/10.1016/j.icte.2023.11.008 |
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| author | Salh, Adeeb Ngah, Razali Hussain, Ghasan Ali Alhartomi, Mohammed Boubkar, Salah M. Shah, Nor Shahida Alsulami, Ruwaybih Alzahrani, Saeed |
| author_facet | Salh, Adeeb Ngah, Razali Hussain, Ghasan Ali Alhartomi, Mohammed Boubkar, Salah M. Shah, Nor Shahida Alsulami, Ruwaybih Alzahrani, Saeed |
| author_sort | Salh, Adeeb |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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%. |
| format | Article |
| id | my.uthm.eprints-11761 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | ScienceDirect |
| record_format | eprints |
| spelling | my.uthm.eprints-117612025-01-24T09:26:29Z http://eprints.uthm.edu.my/11761/ Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework Salh, Adeeb Ngah, Razali Hussain, Ghasan Ali Alhartomi, Mohammed Boubkar, Salah M. Shah, Nor Shahida Alsulami, Ruwaybih Alzahrani, Saeed T Technology (General) 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%. ScienceDirect 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11761/1/J17170_8220a34f4585fd4860d50eb6631fae63.pdf 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. https://doi.org/10.1016/j.icte.2023.11.008 |
| spellingShingle | T Technology (General) Salh, Adeeb Ngah, Razali Hussain, Ghasan Ali Alhartomi, Mohammed Boubkar, Salah M. Shah, Nor Shahida Alsulami, Ruwaybih Alzahrani, Saeed Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title | Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title_full | Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title_fullStr | Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title_full_unstemmed | Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title_short | Bandwidth Allocation of URLLC for Real-time Packet Traffic in B5G: A Deep-RL Framework |
| title_sort | bandwidth allocation of urllc for real-time packet traffic in b5g: a deep-rl framework |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/11761/1/J17170_8220a34f4585fd4860d50eb6631fae63.pdf http://eprints.uthm.edu.my/11761/ https://doi.org/10.1016/j.icte.2023.11.008 |
| url_provider | http://eprints.uthm.edu.my/ |