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Related Experiment Video

Updated: Oct 11, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

Bhanu Priya1, Jyoteesh Malhotra1

  • 1Department of Engineering and Technology, GNDU RC, Jalandhar, India.

Journal of Ambient Intelligence and Humanized Computing
|December 1, 2021
PubMed
Summary

This study introduces an intelligent system for smart hospitals, optimizing network connections for better remote healthcare. It ensures a high Quality of Experience (QoE) for personalized patient services.

Keywords:
Artificial IntelligenceDouble deep reinforcement learningEdge computingQoEResource utilisation factorSoftware-defined wireless networking

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Area of Science:

  • Healthcare technology
  • Telecommunications
  • Computer science

Background:

  • The COVID-19 pandemic accelerated the digital transformation of healthcare towards smart hospitals.
  • 5G and B5G networks are crucial for advanced personalized and remote healthcare services.
  • Current Radio Access Technology (RAT) selection methods struggle to maintain Quality of Service (QoS) and Quality of Experience (QoE) in these evolving networks.

Purpose of the Study:

  • To propose an intelligent Quality of Experience (QoE)-aware RAT selection architecture for 5G-enabled healthcare networks.
  • To enhance service orchestration flexibility and agility in smart hospital connectivity.
  • To improve personalized user experiences and resource utilization in intelligent healthcare systems.

Main Methods:

  • Developed a novel architecture integrating Software-Defined Wireless Networking (SDWN) and edge computing.
  • Employed invalid action masking and multi-agent reinforcement learning for QoE-optimized RAT selection.
  • Utilized analytical evaluation to validate the proposed scheme's performance.

Main Results:

  • The proposed architecture significantly improves Quality of Experience (QoE) for users.
  • Demonstrated efficient resource utilization within the 5G-enabled healthcare network.
  • Outperformed existing RAT selection schemes in QoE provisioning and QoS maintenance.

Conclusions:

  • The intelligent QoE-aware RAT selection architecture is effective for 5G healthcare networks.
  • The approach enhances personalized healthcare services through optimized connectivity.
  • This model represents a robust solution for the challenges of service orchestration in intelligent healthcare systems.