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

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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.

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Acknowledgements

Author would like to thank University Grant Commission, New Delhi for Junior Research Fellowship.

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    Bhanu Priya & Jyoteesh Malhotra

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Priya, B., Malhotra, J. 5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network. J Ambient Intell Human Comput 14, 8387–8408 (2023). https://doi.org/10.1007/s12652-021-03606-x

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