5G-TPS: A Two-Phase Real-Time Scheduling and Adaptation Framework for 5G Radio Access Networks

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, wide coverage, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling data flows to meet their hard real-time requirements in industrial applications. In this paper, we first leverage a 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantee through time-frequency resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct a schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. For large-scale multi-cell 5G industrial systems with cloud radio access network (C-RAN) architecture, we further introduce a user association algorithm respecting the real-time requirements of individual user equipment (UEs). Extensive experimental studies show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.