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.
ABSTRACT
The COVID-19 outburst has encouraged the adoption of Internet of Medical Things (IoMT) network to empower the antiquated healthcare system and alleviate the health care costs. To realise the functionalities of the IoMT network, 5G heterogeneous networks emerged as an exemplary connectivity solution as it facilitates diversified service provisioning in the service delivery model at more convenient care. However, the crucial challenge for 5G heterogeneous wireless connectivity solution is to facilitate agile differentiated service provisioning. Lately, considerable research endeavour has been noted in this direction but multiservice consideration and battery optimisation have not been addressed. Motivated by the gaps in the existing literature, an intelligent radio access technology selection approach has been proposed to ensure Quality of Service provisioning in a multiservice scenario on the premise of battery optimisation. In particular, the proposed approach leverages the concept of Double Deep Reinforcement Learning to attain an optimal network selection policy. Eventually, the proposed approach corroborated by the rigorous simulations demonstrated a substantial improvement in the overall system utility. Subsequently, the performance evaluation underlines the efficacy of the proposed scheme in terms of convergence and complexity.