5G in healthcare: Matching game-empowered intelligent medical network slicing

Abstract

Improving the healthcare system is imperative for increasing efficiency and reducing costs. The rapid adoption of Internet of Medical Things (IoMT) has facilitated a broad range of healthcare applications and services, from real-time and critical care monitoring to telemedicine. These services typically have highly distinct quality of service (QoS) requirement for communication networks and thus cannot be served with a uniform network. In such a context, network slicing technologies in 5G can be employed to create virtually independent and customized communication networks for these use cases to meet their QoS requirement. In this wok, we model network slicing for healthcare services as a virtual network embedding (VNE) problem and propose a two-sided matching theory-based virtual network embedding (MT-VNE) solution. In MT-VNE, four novel preference indexes are devised to construct differential preference lists. The deferred acceptance and modified shortest path-based algorithms are utilized to perform the virtual node and link mapping, respectively. Extensive simulations demonstrate that MT-VNE outperforms other baselines in terms of accepting more healthcare services and effectively utilizing physical network resources. Moreover, MT-VNE also significantly reduces service embedding time.

Keywords

• Medical network slicing (MNS)
• 5G healthcare
• Quality of service(QoS)
• Virtual network embedding(VNE)
• Matching theory