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To date, model-based reliable communication with low latency is of paramountimportance for time-critical wireless control systems. In this work, we studythe downlink (DL) controller-to-actuator scheduling problem in a wirelessindustrial network such that the outage probability is minimized. In contrastto the existing literature based on well-known stationary fading channelmodels, we assume an arbitrary and unknown channel fading model, which isavailable only via samples. To overcome the issue of limited data samples, weinvoke the generative adversarial network framework and propose an onlinedata-driven approach to jointly schedule the DL transmissions and learn thechannel distributions in an online manner. Numerical results show that theproposed approach can effectively learn any arbitrary channel distribution andfurther achieve the optimal performance by using the predicted outageprobability.

Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach