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 of tasks and the relationship between prediction and communication into account. In this paper, we propose a task-oriented prediction and communication co-design framework, where the reliability of the system depends on prediction errors and packet losses in communications. The goal is to minimize the required radio resources subject to the low-latency and high-reliability requirements of various tasks. Specifically, we consider the just noticeable difference (JND) as a performance metric for the haptic communication system. We collect experiment data from a real-world teleoperation testbed and use time-series generative adversarial networks (TimeGAN) to generate a large amount of synthetic data. This allows us to obtain the relationship between the JND threshold, prediction horizon, and the overall reliability including communication reliability and prediction reliability. We take 5G New Radio as an example to demonstrate the proposed framework and optimize bandwidth allocation and data rates of devices. Our numerical and experimental results show that the proposed framework can reduce wireless resource consumption up to 77.80% compared with a task-agnostic benchmark.

翻译：预测最近被视为满足长距离触觉通信低延迟和高可靠性要求的一种有前途的方法。然而，现有大多数方法未考虑任务特征及预测与通信之间的关系。本文提出了一种任务导向的预测与通信协同设计框架，其中系统的可靠性取决于预测误差和通信中的数据包丢失。目标是在满足各种任务的低延迟和高可靠性要求的前提下，最小化所需无线电资源。具体而言，我们将恰可察觉差异（JND）作为触觉通信系统的性能指标。我们从真实远程操作测试平台收集实验数据，并使用时间序列生成对抗网络（TimeGAN）生成大量合成数据。这使得我们能够获得JND阈值、预测范围以及包括通信可靠性和预测可靠性在内的整体可靠性之间的关系。我们以5G新空口为例展示所提出的框架，并优化带宽分配和设备的数率。我们的数值与实验结果表明，与任务无关的基准相比，所提出的框架可将无线资源消耗降低高达77.80%。