Predictive Dynamic Scheduling for Deterministic Communications in Beyond 5G

Abstract

Next generation wireless networks must sustain deterministic service levels to support emerging time-sensitive applications. The ability to guarantee bounded latencies depends on the efficient management of radio resources. Several studies propose leveraging the native intelligence of future networks to develop predictive schedulers capable of efficiently managing resources. However, existing proposals focus on semi-static scheduling, where resources are reserved based on traffic predictions, and these reservations are susceptible to inefficiencies due to prediction inaccuracies. This study advances the state of the art with a novel predictive dynamic scheduling scheme that avoids such inefficiencies, and leverages traffic predictions to allocate resources to incoming requests that meet their latency requirements while avoiding resources likely to be needed by future predicted packets. Our results demonstrate that the proposed predictive dynamic scheduling effectively supports deterministic communications in scenarios with mixed traffic flows and varying QoS requirements.