Learning-Based Resource Allocation in Industrial IoT Systems

We consider an industrial internet-of-things (IIoT) system with multiple IoT devices, a user equipment (UE), together with a base station (BS) that receives the UE and IoT data. To circumvent the issue of numerous IoT-to-BS connections and to conserve IoT devices' energies, the UE serves as a relay to forward the IoT data to the BS. The UE employs frame-based uplink transmissions, wherein it shares few slots of every frame to relay the IoT data. The IIoT system experiences a transmission failure called outage when IoT data is not transmitted. The unsent UE data is stored in the UE's buffer and is discarded after the storage time exceeds the age threshold. As the UE and IoT devices share the transmission slots, trade-offs exist between system outages and aged UE data loss. To resolve system outage-data ageing challenge, we provide model-free reinforcement learning (RL)-based policies for slot-sharing between UE and IoT data. We compare the performance of the RL-based policies with low complexity heuristic-based slot-sharing schemes which either prioritise the UE data or account only for near-threshold aged UE data or are oblivious to the amount of UE data.