Shapley Features for Robust Signal Prediction in Tactile Internet
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911398638911488 |
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| author | Vahedifar, Mohammad Ali Zhang, Qi |
| author_facet | Vahedifar, Mohammad Ali Zhang, Qi |
| contents | The Tactile Internet (TI) requires ultra-low latency and reliable haptic signal transmission, yet packet loss and delay remain unresolved challenges. We present a novel prediction framework that integrates Gaussian Processes (GP) with a ResNet-based Neural Network, where GP acts as an oracle to recover signals lost or heavily delayed. To further optimize performance, we introduce Shapley Feature Values (SFV), a principled feature selection mechanism that isolates the most informative inputs for prediction. This GP+SFV framework achieves 95.72% accuracy, surpassing the state-of-the-art LeFo method by 11.1%, while simultaneously relaxing TI's rigid delay constraints. Beyond accuracy, SFV operates as a modular accelerator: when paired with LeFo, it reduces inference time by 27%, and when paired with GP, by 72%. These results establish GP+SFV as both a high-accuracy and high-efficiency solution, paving the way for practical and reliable haptic communications in TI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shapley Features for Robust Signal Prediction in Tactile Internet Vahedifar, Mohammad Ali Zhang, Qi Signal Processing The Tactile Internet (TI) requires ultra-low latency and reliable haptic signal transmission, yet packet loss and delay remain unresolved challenges. We present a novel prediction framework that integrates Gaussian Processes (GP) with a ResNet-based Neural Network, where GP acts as an oracle to recover signals lost or heavily delayed. To further optimize performance, we introduce Shapley Feature Values (SFV), a principled feature selection mechanism that isolates the most informative inputs for prediction. This GP+SFV framework achieves 95.72% accuracy, surpassing the state-of-the-art LeFo method by 11.1%, while simultaneously relaxing TI's rigid delay constraints. Beyond accuracy, SFV operates as a modular accelerator: when paired with LeFo, it reduces inference time by 27%, and when paired with GP, by 72%. These results establish GP+SFV as both a high-accuracy and high-efficiency solution, paving the way for practical and reliable haptic communications in TI systems. |
| title | Shapley Features for Robust Signal Prediction in Tactile Internet |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.21032 |