Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet
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| Format: | Preprint |
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2026
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| _version_ | 1866911582570676224 |
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| author | Vahedifar, Mohammad Ali Nazari, Mojtaba Zhang, Qi |
| author_facet | Vahedifar, Mohammad Ali Nazari, Mojtaba Zhang, Qi |
| contents | The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instability. To address this, we propose the Mode-Domain Architecture (MDA), a bilateral predictive neural network architecture designed to restore missing signals on both the human and robot sides. Unlike conventional models that extract features implicitly from raw data, MDA utilizes a novel Continuous-Orthogonal Mode Decomposition framework. By integrating an orthogonality constraint, we overcome the pervasive issue of "mode overlapping" found in state-of-the-art decomposition methods. Experimental results demonstrate that this structured feature extraction achieves high prediction accuracies of 98.6% (human) and 97.3% (robot). Furthermore, the model achieves ultra-low inference latency of 0.065 ms, significantly outperforming existing benchmarks and meeting the stringent real-time requirements of haptic teleoperation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09446 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet Vahedifar, Mohammad Ali Nazari, Mojtaba Zhang, Qi Signal Processing Machine Learning The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instability. To address this, we propose the Mode-Domain Architecture (MDA), a bilateral predictive neural network architecture designed to restore missing signals on both the human and robot sides. Unlike conventional models that extract features implicitly from raw data, MDA utilizes a novel Continuous-Orthogonal Mode Decomposition framework. By integrating an orthogonality constraint, we overcome the pervasive issue of "mode overlapping" found in state-of-the-art decomposition methods. Experimental results demonstrate that this structured feature extraction achieves high prediction accuracies of 98.6% (human) and 97.3% (robot). Furthermore, the model achieves ultra-low inference latency of 0.065 ms, significantly outperforming existing benchmarks and meeting the stringent real-time requirements of haptic teleoperation. |
| title | Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2604.09446 |