Status Prediction for Age of Information Oriented Short-Packet Transmission in Industrial IoT

Age of information (AoI), which measures the freshness of information, is a critical performance metric of timesensitive applications of industrial Internet of things (IIoT) with short-packet transmission (SPT). In this paper, we investigate the suitability of predicting the status updates at source and sending them to destination in advance for AoI oriented SPT systems, in the presence of prediction error as well as transmission error. A predictive transmission scheme is proposed, where proactive transmission termination is adopted as soon as a prediction error is detected, and also multiple correlated features of the status is considered. A closed-form expression for the average AoI with respect to prediction horizon (related to prediction error probability) and blocklength (related to transmission error probability) is derived for the multi-feature source scenario. Also, the prediction error probability with respect to prediction horizon is derived in closed form. It is proved that the average AoI performance can benefit from status prediction, even under high prediction error probability. Simulation results demonstrate the correctness of the analytical results, and show that the proposed prediction scheme outperforms the prediction approach with no transmission termination, and there exists an optimal prediction horizon in terms of average AoI. A tight approximation of the optimal prediction horizon is derived for the special case of single-feature status, which achieves a near-optimal performance, with a much lower complexity than exhaustive search.