Predictive Dynamic Scheduling for Deterministic Communications in Beyond 5G
2026-06-15 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the challenge of managing wireless network resources to guarantee timely data delivery for sensitive applications. They point out that previous methods rely on semi-static scheduling, which can waste resources due to inaccurate traffic predictions. Their new approach is a predictive dynamic scheduling system that uses traffic forecasts to allocate resources more efficiently, ensuring timely communication without blocking future important data. Their results show this method works well even when different types of data with various latency needs are mixed together.
wireless networksresource managementlatencypredictive schedulingtraffic predictionquality of service (QoS)deterministic communicationsdynamic schedulingtime-sensitive applications
Authors
Syed Morsleen Riaz, M. Carmen Lucas-Estañ, Baldomero Coll-Perales, Javier Gozalvez
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.