Pilot plays a vital role in acquiring channels, but leading to high pilot overhead in a short packet that includes a pilot part and an effective blocklength part. The removal of pilots for channel estimation is an effective way to reduce age of information (AoI) in short-packet transmissions, by increasing transmission power in effective blocklength and reducing the optimum blocklength, given the total fixed transmission power. We investigate spatial correlation to minimize AoI for unmanned aerial vehicle (UAV)-enabled wireless data collection and wireless power transfer (WPT) in short-packet transmissions, by reducing the number of pilots as much as possible, while guaranteeing block error rate (BLER) performance. A number of sensors in proximity are organized into a cluster. Only the reference sensor needs pilots to estimate channel, while the other sensors utilize spatial correlation to estimate their channels in a cluster requiring no pilots. Channel estimation error is introduced, and considered in BLER and in cluster region determination via elevation angle of UAV. The proposed approach tolerates the variance of channel estimation error up to 0.9, and in such case still provides AoI performance better than the existing work where each sensor requires pilots to estimate channels.

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%.

Industrial metaverse aims to build an immersive virtual space that can interact with physical space in real-time. Accurate reconstruction of the time-varying physical processes in virtual space is crucial to the realization of industrial metaverse, especially under short-packet transmission (SPT). In this paper, we investigate the suitability of inferring the real-time data of a sensor from the spatially correlated sensor data for SPT in industrial metaverse, in the presence of transmission delay and error as well as imperfect spatial correlation among data. Closed-form expressions for the average mean squared error (MSE) with and without inference are derived. Also, a tight approximation for the average MSE with inference is presented. A closed-form threshold that the inference-aware reconstruction outperforms the case without inference is derived in terms of the average received SNR. Simulation results verify the analytical results and demonstrate that the inference-aware reconstruction enables an average MSE reduction of 32% over the case without inference, and is suitable to the scenarios with low average received SNR, long period, short blocklength and strong mean squared spatial correlation.

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.

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.