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

Prediction has recently been considered as a promising approach to meet low-latency and high-reliability requirements in long-distance haptic communications. However, most of the existing methods did not take features tasks relationship between prediction communication into account. In this paper, we propose task-oriented co-design framework, where reliability system depends on errors packet losses The goal is minimize required radio resources subject various tasks. Specifically, consider just noticeable difference (JND) performance metric for system. We collect experiment data from real-world teleoperation testbed use xmlns:xlink="http://www.w3.org/1999/xlink">time-series generative adversarial networks (TimeGAN) generate large amount synthetic data. This allows us obtain JND threshold, horizon, overall including reliability. 5G New Radio an example demonstrate proposed framework optimize bandwidth allocation rates devices. Our numerical experimental results show that can reduce wireless resource consumption up $77.80\%$ compared with task-agnostic benchmark.