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iMnet: Intelligent RAT Selection Framework for 5G Enabled IoMT Network.

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Summary

This study introduces an intelligent network selection approach for the Internet of Medical Things (IoMT) using Double Deep Reinforcement Learning. It optimizes Quality of Service and battery life in 5G networks for better healthcare delivery.

Keywords:
5GDouble deep reinforcement learningEdge computingIoMTSoftware-defined wireless networking

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Area of Science:

  • Telecommunications Engineering
  • Health Informatics
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic accelerated the adoption of the Internet of Medical Things (IoMT) to enhance healthcare systems and reduce costs.
  • 5G heterogeneous networks offer a promising connectivity solution for IoMT, enabling diverse services and convenient patient care.
  • A key challenge in 5G IoMT is providing agile, differentiated services while optimizing battery consumption, an area not fully addressed in prior research.

Purpose of the Study:

  • To propose an intelligent radio access technology selection approach for multiservice IoMT scenarios.
  • To ensure Quality of Service (QoS) provisioning while optimizing battery life within 5G heterogeneous networks.
  • To address the limitations of existing research concerning multiservice considerations and battery optimization in IoMT.

Main Methods:

  • Leveraging Double Deep Reinforcement Learning (DDRL) to develop an optimal network selection policy.
  • Implementing a multiservice scenario to evaluate the proposed approach under diverse demands.
  • Conducting rigorous simulations to validate the effectiveness of the proposed intelligent selection scheme.

Main Results:

  • The proposed Double Deep Reinforcement Learning approach demonstrated a substantial improvement in overall system utility for IoMT networks.
  • The intelligent selection scheme effectively ensures Quality of Service provisioning in complex, multiservice environments.
  • Significant enhancements in battery optimization were achieved without compromising service quality.

Conclusions:

  • The developed intelligent radio access technology selection method effectively balances QoS and battery optimization in 5G IoMT networks.
  • The approach provides a robust solution for agile, differentiated service provisioning in advanced healthcare systems.
  • Simulation results confirm the scheme's efficacy, highlighting its convergence and manageable complexity for practical deployment.