A Learning Approach for Production-Aware 5G Slicing in Private Industrial Networks

Industrial scenarios comprise multiple devices executing periodic, mutually-dependent tasks with challenging communications requirements. 5G technology and RAN slicing make it possible to accommodate such requirements. The predictability of the environment can be exploited to improve the network efficiency and performance. We leverage Deep Reinforcement Learning to design a "production-aware" agent based on the Deep Deterministic Policy Gradient algorithm. The proposed scheme combines production and network information to select the spectrum configuration of each slice. In details, by exploiting the knowledge about the upcoming production tasks, the agent can effectively predict the required per-slice spectrum consumption in order to boost the service provisioning reliability and limit the spectrum over-provisioning. We compare the performance of this approach with a "production-unaware" agent and with the optimal spectrum allocation. Simulations show how our solution provides a per-slice reliability in terms of meeting the latency requirements higher than the one provided by the "production-unaware" agent. Moreover, it ensures a tight approximation of the optimal slice spectrum allocation.