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Driven by mission-critical applications in modern industrial systems, the 5thgeneration (5G) communication system is expected to provide ultra-reliablelow-latency communications (URLLC) services to meet the quality of service(QoS) demands of industrial applications. However, these stringent requirementscannot be guaranteed by its conventional dynamic access scheme due to thecomplex signaling procedure. A promising solution to reduce the access delay isthe pre-allocation scheme based on the semi-persistent scheduling (SPS)technique, which however may lead to low spectrum utilization if the allocatedresource blocks (RBs) are not used. In this paper, we aim to address this issueby developing DPre, a predictive pre-allocation framework for uplink accessscheduling of delay-sensitive applications in industrial process automation.The basic idea of DPre is to explore and exploit the correlation of dataacquisition and access behavior between nodes through static and dynamiclearning mechanisms in order to make judicious resource per-allocationdecisions. We evaluate the effectiveness of DPre based on several monitoringapplications in a steel rolling production process. Simulation resultsdemonstrate that DPre achieves better performance in terms of the predictionaccuracy, which can effectively increase the rewards of those reservedresources.

Predictive Pre-allocation for Low-latency Uplink Access in Industrial Wireless Networks