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Prediction has recently been considered as a promising approach to meetlow-latency and high-reliability requirements in long-distance hapticcommunications. However, most of the existing methods did not take features oftasks and the relationship between prediction and communication into account.In this paper, we propose a task-oriented prediction and communicationco-design framework, where the reliability of the system depends on predictionerrors and packet losses in communications. The goal is to minimize therequired radio resources subject to the low-latency and high-reliabilityrequirements of various tasks. Specifically, we consider the just noticeabledifference (JND) as a performance metric for the haptic communication system.We collect experiment data from a real-world teleoperation testbed and usetime-series generative adversarial networks (TimeGAN) to generate a largeamount of synthetic data. This allows us to obtain the relationship between theJND threshold, prediction horizon, and the overall reliability includingcommunication reliability and prediction reliability. We take 5G New Radio asan example to demonstrate the proposed framework and optimize bandwidthallocation and data rates of devices. Our numerical and experimental resultsshow that the proposed framework can reduce wireless resource consumption up to77.80% compared with a task-agnostic benchmark.

Task-Oriented Prediction and Communication Co-Design for Haptic Communications