Assessment of Key 5G RAN Features for Integrated Services Provisioning in a Smart City Environment

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Abstract

The Fifth Generation (5G) network is expected to support three main service categories namely enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC) and massive Machine Type Communications (mMTC), where each service group has a different Quality of Service (QoS) requirements i.e. the eMBB service group has a high throughput requirement, the URLLC service group require very low-latency and highly reliable transmissions and the mMTC service group do not have a strict performance requirement but have massive connection of devices in the network.

5G enables many vertical domains with these three service categories, such as, smart cities. In a smart city environment, there are applications from all three service categories such as massive connectivity of the sensors for waste managements or monitoring environmental conditions, video surveillance along the city streets and many more. This study addresses the problem of managing applications from the three service categories on the same physical network infrastructure at the Radio Access Network (RAN). Two different scenarios, one with and one without an emergency incident, are considered to find the impact of the incident in the network.

In 5G networks, many new features are introduced such as, flexible numerology, mini-slot based scheduling, BandWidth Parts (BWPs) and RAN slicing. The key objective of this study is to assess the 5G RAN features in terms of achieving the performance requirements of the considered applications, simultaneously. To do so, different RAN configurations are modelled where, a RAN configuration consists of one or multiple RAN features. The evaluation is done by simulating the different possible RAN configurations. The simulations are performed using an existing 5G system-level simulator which is substantially upgraded with the 5G RAN features and is modified to the considered smart city urban macro-cellular environment and with the considered traffic models for each considered application.

To evaluate the performance of each considered application, different performance metrics are defined based on the application requirements. The benefits and/or losses of different RAN features are found and then different RAN configurations are considered with the combinations of RAN features based on the evaluation of each RAN feature. For all different RAN configurations with combination of features, the performance metrics are evaluated and compared with each other to determine the best-performing configurations for the smart city environment, for the scenarios with and without an incident.