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5GhNet：一种用于支持5G的医疗网络的智能感知QoE的RAT选择框架。

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

作者信息

Priya Bhanu, Malhotra Jyoteesh

机构信息

Department of Engineering and Technology, GNDU RC, Jalandhar, India.

出版信息

J Ambient Intell Humaniz Comput. 2023;14(7):8387-8408. doi: 10.1007/s12652-021-03606-x. Epub 2021 Nov 26.

DOI:10.1007/s12652-021-03606-x
PMID:34849173
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8617375/
Abstract

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

摘要

新冠疫情推动了过时的医疗系统向智能医院的数字化转型，实现了个性化和远程医疗服务。为增强这些智能医疗系统的功能，5G和B5G异构网络已成为一种强大且可靠的解决方案。但5G和B5G连接解决方案的关键挑战在于确保具有公认体验质量（QoE）的灵活且敏捷的服务编排。然而，现有的无线接入技术（RAT）选择策略在QoE提供和服务质量（QoS）维护方面无能为力。因此，针对支持5G的医疗网络，提出了一种基于软件定义无线网络（SDWN）和边缘计算的智能QoE感知RAT选择架构。所提出的模型利用无效动作屏蔽和多智能体强化学习的原理，以更快地收敛到QoE优化的RAT选择策略。分析评估验证了所提出的方案在通过高效资源利用增强个性化用户体验方面优于其他现有方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/b703b9ffa38c/12652_2021_3606_Fig17_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/6c5b96efb542/12652_2021_3606_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/2096e689a34f/12652_2021_3606_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/6e484e5b4952/12652_2021_3606_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/acb8c0d5c593/12652_2021_3606_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/8fe6dc379a12/12652_2021_3606_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8341/8617375/77690fd87a94/12652_2021_3606_Fig11_HTML.jpg
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