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

Abstract

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.

A Prediction-Enhanced Physical-to-Virtual Twin Connectivity Framework for Human Digital Twin

“…The lack of wireless resources was overcome in [17] through the proposed Gaussian process regression-based prediction and machine learning-enabled solution. In [18], the authors proposed a task-oriented prediction and communication co-design scheme with reliability formulated as a function of both prediction errors and packet losses, while a predictionenhanced model was formulated in [19] to improve the realtime vehicular communication link by sending the predicted data to the receiver in advance. A similar solution was presented for the time-critical industrial internet of things applications in [20] to enhance the performance of the age of information for short-packet transmission.…”

“…To characterize the prediction process, we need to obtain the relationship between the prediction horizon and the prediction error. For explanation purposes, we consider a general linear prediction approach as in [5], [14], [18], where the transition from any state t to state t + 1 of any typical PT X m follows the state transition function [25] as…”

“…represents the CDF of the standard Gaussian distribution with mean and variance of zero and one, respectively. From (10) and (18),…”