A Hybrid Centralized Uplink Scheduler for Low Latency in 5G Industrial IoT Networks

One of the key requirements for future fifth generation (5G) industrial Internet of Things (IIoT) networks will be to deliver low latency to support different production processes. To this end, 5G new radio (NR) provides configured grant (CG) scheduling for periodic traffic, in addition to conventional grant-based scheduling (GBS). However, in view of the complexities introduced by spatio-temporal traffic correlations in IIoT, a fixed scheduler configuration may be suboptimal: GBS introduces excessive signaling overhead, while CG leads to inefficient resource utilization and latency degradation when traffic is not perfectly periodic. To solve these critical issues, we propose a hybrid centralized uplink scheduler (HCUS), a new scheduling framework that dynamically learns the type of traffic generated by user equipment (UE) and adapts resource allocation accordingly. HCUS operates per-UE and dynamically switches between GBS and CG, optimizing resource allocation while preserving low end-to-end (E2E) latency. We consider both mixed periodic and aperiodic uplink traffic to model different network load conditions and IIoT applications. Extensive simulations show that HCUS achieves up to four times lower latency than GBS and CG while maintaining high reliability, even considering traffic correlations or periodicity changes, making it a robust and scalable solution for next-generation IIoT scenarios.