论文标题
超级可靠的低延迟工业物联网中的数据驱动预测调度：一种生成对抗网络方法
Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach
论文作者
论文摘要
迄今为止，基于模型的可靠通信与低潜伏期对于时间关键的无线控制系统至关重要。在这项工作中，我们研究了无线工业网络中的下行链路（DL）控制器到实施者调度问题，以使中断概率最小化。与基于众所周知的固定褪色通道模型的现有文献相反，我们假设一个任意且未知的通道褪色模型，仅通过样品可用。为了克服有限数据样本的问题，我们调用了生成对抗网络框架，并提出了一种在线数据驱动的方法，以共同安排DL传输并以在线方式学习渠道分布。数值结果表明，提出的方法可以有效地学习任何任意通道分布，并使用预测的中断概率进一步实现最佳性能。
To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study the downlink (DL) controller-to-actuator scheduling problem in a wireless industrial network such that the outage probability is minimized. In contrast to the existing literature based on well-known stationary fading channel models, we assume an arbitrary and unknown channel fading model, which is available only via samples. To overcome the issue of limited data samples, we invoke the generative adversarial network framework and propose an online data-driven approach to jointly schedule the DL transmissions and learn the channel distributions in an online manner. Numerical results show that the proposed approach can effectively learn any arbitrary channel distribution and further achieve the optimal performance by using the predicted outage probability.
