This study proposes a two-step ML-based multislice radio resource allocation framework for 5G networks, specifically for emergency scenarios and featuring a good tradeoff between complexity and performance. In the first step, call-level resource demands are predicted using supervised ML, which are then aggregated to predict slice-specific resource demands. An innovative method is included in this step to ensure the collection of representative training data for the supervised ML. In the second step, a contextual multi-armed bandit reinforcement learning model is applied to derive the resource allocation among the slices based on the slice-specific resource demand predictions. The simulation results show that the proposed framework outperforms alternative solutions in the defined utility values for priority emergency traffic at the cost of modest performance sacrifice of the background traffic.

A suitability assessment and performance optimisation is presented of narrowband Internet of Things (NB-IoT) cellular technology for use in smart energy distribution networks. The focus is on the reliable and timely delivery of outage restoration and management (ORM) messages at the event of a local or regional power outage. Both the cellular NB-IoT and the energy distribution networks are modelled in a system-level simulator, which is used to carry out an extensive sensitivity analysis of the ORM service performance w.r.t. various radio network configurations in different environments. In particular, different packet schedulers are proposed and analysed, addressing device prioritization and subcarrier allocation as essential mechanisms in optimizing the service performance. Furthermore, we consider all three possible NB-IoT spectral deployment modes: in-band, guard-band and stand-alone deployment. Results show that, with a proposed near-optimal radio network configuration, the reliability of the ORM message delivery is close to 100% for the majority of power outage scenarios, while the observed 95th transfer delay percentile for the ORM messages is within the acceptable limit of 20 s. The study concludes that indeed NB-IoT is a suitable technology for supporting ORM services in smart energy distribution networks.

In this paper, we assess the suitability of NB-IoT (Narrowband Internet of Things) cellular technology for smart grid applications, concentrating on the reliable and timely delivery of Outage Restoration Management (ORM) messages at the event of a local or regional power outage. Using system-level simulations modelling of both the cellular NB-IoT and the energy distribution networks for different environments, we present an extensive sensitivity analysis of the ORM service performance w.r.t. various radio network configurations. In particular, we propose and analyze different packet schedulers, an essential mechanism in optimizing the service performance. A key outcome of the study is the conclusion that indeed NB-IoT is a suitable technology for supporting ORM services in smart grids, accompanied with a proposed near-optimal radio network configuration to best do so.