为 B5G 中的实时数据包流量分配 URLLC 的带宽:Deep-RL 框架

IF 4.1 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Adeeb Salh , Razali Ngah , Ghasan Ali Hussain , Mohammed Alhartomi , Salah Boubkar , Nor Shahida M. Shah , Ruwaybih Alsulami , Saeed Alzahrani
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引用次数: 0

摘要

考虑到物联网(IoT)设备的能量有限。我们根据功率控制和信道有限块长的联合优化来分配资源,以保证严格的服务质量(QoS)。为了实现大量的到达率,我们提出了基于对抗训练的生成对抗网络(AT-GANs),它利用大量的极端事件来提供高可靠性并实时调整真实数据。仿真结果表明,AT-GAN 的深度强化学习(Deep-RL)可以消除瞬时训练时间。因此,AT-GAN 可保持高于 99.9999% 的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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%.

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来源期刊
ICT Express
ICT Express Multiple-
CiteScore
10.20
自引率
1.90%
发文量
167
审稿时长
35 weeks
期刊介绍: The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.
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