5G for telehealth : utility of local mobile network operator architecture and network slicing

Abstract

This thesis studies two novel technologies enabled by network softwarization, namely Local 5G Operator (L5GO) networks and network slicing for 5-th Generation (5G) and beyond 5G networks. This study proposes a L5GO network architecture for delay critical future tele- health services, considering two use cases on Augmented Reality Assisted Surgery (ARAS) and Robotic Aided Surgery (RAS). Study compares the latency performance of the proposed Local 5G Operator (L5GO) architecture with a traditional legacy network and a network equipped with Mobile Edge Computing (MEC). The results highlight the unique advan- tages of utilizing an L5GO to cater the communication needs of delay critical telehealth, compared to a traditional network. To the end of network slicing, which enables the cre- ation of multiple logical independent networks on physical networking infrastructure can be classified as vertical and horizontal slicing. It is a key methodology to deliver guaranteed Quality of Service (QoS) to a multitude of use cases with varying resource requirements, and hence considered vital for 5G and beyond 5G mobile networks. This thesis focuses on the optimal resource allocation in a 5G network that utilizes network slicing, and caters to the communication requirements of a smart hospital. The study compares the performance of the slicing methods through solving two convex optimization problems, considering several smart hospital scenarios that differ from each other based on their medical speciality. The results are used to draw insights on the most appropriate slicing approach for each setup.