To promote the revolution of Industrial Internet of Things, the next generation communication system is expected to provide latency critical services in industry. However, for the traditional downlink-centric cellular systems, the timely delivery of packets cannot be guaranteed by the default dynamic access scheme due to complex signaling procedure. A promising solution to low-latency access is the resource pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, however at the expense of low spectrum utilization. Aiming to make those pre-allocated resources more rewarding, a so-called DPre, a predictive pre-allocation scheme based on learning for low-latency uplink access in industrial wireless networks, is proposed in this paper. It intelligently explores the correlation of devices’ access behavior and device utility diversity through sequential learning. Thus, flexible and judicious per-allocation decisions in both time and frequency domains can be made in an on-demand manner. Moreover, with the proposed temporal-spatial utility metric, DPre is guaranteed to reserve for more informative devices. Both theoretical analysis and simulation validate its high spectrum utilization through accurate prediction and the potential to pre-allocate for valuable packets.

中文翻译：

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一种基于学习的工业无线网络低延迟接入预分配方案

为了推动工业物联网的革命，下一代通信系统有望在工业中提供延迟关键的服务。然而，对于传统的以下行链路为中心的蜂窝系统，由于复杂的信令过程，默认的动态接入方案无法保证数据包的及时传递。低延迟接入的一个有前途的解决方案是基于半持久调度 (SPS) 技术的资源预分配方案，但是以低频谱利用率为代价。为了使这些预先分配的资源更有价值，本文提出了一种所谓的 DPre，一种基于学习的工业无线网络中低延迟上行链路接入的预测性预分配方案。它通过顺序学习智能地探索设备访问行为与设备效用多样性的相关性。因此，可以按需方式在时域和频域中做出灵活且明智的按分配决策。此外，通过提出的时空效用指标，DPre 保证为更多信息设备保留。理论分析和仿真都通过准确预测和预分配有价值数据包的潜力来验证其高频谱利用率。