触覚通信のためのタスク指向予測・通信協調設計
Task-Oriented Prediction and Communication Co-Design for Haptic Communications
触覚通信において、タスクの特性と予測・通信の関係を考慮し、低遅延・高信頼性を満たしつつ無線リソースを最小化する協調設計フレームワークを提案した。実機データとTimeGANでJND閾値と予測・通信信頼性の関係をモデル化し、5G NRで最大77.80%のリソース削減を実証した。
著者: Burak Kizilkaya, Changyang She, Guodong Zhao, Muhammad Ali Imran
分類: cs.RO, cs.LG, cs.NI, cs.SY, eess.SY
原文アブストラクト
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.
