LoRaWAN has emerged as a popular IoT commu- nications technology. It comes with three classes of operation: A, B, and C. Although many IoT use-cases, like Firmware-over- the-Air updates, require multicast, Class A cannot be used for that purpose. Class C can, but consumes a lot of energy. This leaves Class B. In this paper, we investigate Class B multicast and its scalability properties. Issues like multicast member capacity, beacon blocking, and beacon collisions are highlighted, and several approaches to mitigate them are proposed: (1) “Ping-Slot Relaying,” to allow for more multicast members, (2) a scheduling approach indicating when to best send multicast packets, and (3) “Dynamic Region Formation” to coordinate the sending of beacons over multiple gateways. The proposed solutions do not require any modifications to the LoRaWAN protocol.@en

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.

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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.

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