A multi-agent federated reinforcement learning-based optimization of quality of service in various LoRa network slices

The innovations heralded through the implementation of next-generation 5G (Fifth-Generation) networks provide an opportunity for the efficient coexistence of heterogeneous services distributed by a single physical virtualized infrastructure. Indeed, thanks to the possibility of virtualization of network functions implemented in 5G networks, the management of physical resources will be able to become more flexible and users will be able to benefit from a service customization to satisfy their demand in terms of energy efficiency, throughput and communication reliability. However, these advances are not without constraints. The management of physical and virtual resources will become more complex given the number of connected objects which will increase, generating a large volume of data to manage. This therefore requires the implementation of much more intelligent systems in the network controllers in order to guarantee the QoS (Quality of Service) of the communications. Thus, an important axis of research is now oriented towards artificial intelligence techniques, more precisely reinforcement learning to overcome this problem. Driven by this context, we are directing our research towards improving the QoS offered to users of connected objects by proposing an optimization model based on network slicing and federated reinforcement learning in order to minimize energy consumption, maximize user throughputs and reduce latency during communications between LoRa (Long Range) devices. The results obtained by the simulations carried out in a realistic framework clearly demonstrate that our proposal optimizes the traffic in each network slice and also for the individual user.