Prediction-Powered Communication with Distortion Guarantees
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912614800424960 |
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| author | Zecchin, Matteo Ganesan, Unnikrishnan Kunnath Durisi, Giuseppe Popovski, Petar Simeone, Osvaldo |
| author_facet | Zecchin, Matteo Ganesan, Unnikrishnan Kunnath Durisi, Giuseppe Popovski, Petar Simeone, Osvaldo |
| contents | The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscape is motivating a rethinking of classical Shannon information theory that emphasizes semantic and task-oriented paradigms. In this paper, we study a prediction-powered communication setting, in which devices, equipped with artificial intelligence (AI)-based predictors, communicate under zero-delay constraints with strict distortion guarantees. Two classes of distortion measures are considered: (i) outage-based metrics, suitable for tasks tolerating occasional packet losses, such as real-time control or monitoring; and (ii) bounded distortion metrics, relevant to semantic-rich tasks like text or video transmission. We propose two zero-delay compression algorithms leveraging online conformal prediction to provide per-sequence guarantees on the distortion of reconstructed sequences over error-free and packet-erasure channels with feedback. For erasure channels, we introduce a doubly-adaptive conformal update to compensate for channel-induced errors and derive sufficient conditions on erasure statistics to ensure distortion constraints. Experiments on semantic text compression validate the approach, showing significant bit rate reductions while strictly meeting distortion guarantees compared to state-of-the-art prediction-powered compression methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_24373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction-Powered Communication with Distortion Guarantees Zecchin, Matteo Ganesan, Unnikrishnan Kunnath Durisi, Giuseppe Popovski, Petar Simeone, Osvaldo Information Theory Machine Learning Signal Processing The development of 6G wireless systems is taking place alongside the development of increasingly intelligent wireless devices and network nodes. The changing technological landscape is motivating a rethinking of classical Shannon information theory that emphasizes semantic and task-oriented paradigms. In this paper, we study a prediction-powered communication setting, in which devices, equipped with artificial intelligence (AI)-based predictors, communicate under zero-delay constraints with strict distortion guarantees. Two classes of distortion measures are considered: (i) outage-based metrics, suitable for tasks tolerating occasional packet losses, such as real-time control or monitoring; and (ii) bounded distortion metrics, relevant to semantic-rich tasks like text or video transmission. We propose two zero-delay compression algorithms leveraging online conformal prediction to provide per-sequence guarantees on the distortion of reconstructed sequences over error-free and packet-erasure channels with feedback. For erasure channels, we introduce a doubly-adaptive conformal update to compensate for channel-induced errors and derive sufficient conditions on erasure statistics to ensure distortion constraints. Experiments on semantic text compression validate the approach, showing significant bit rate reductions while strictly meeting distortion guarantees compared to state-of-the-art prediction-powered compression methods. |
| title | Prediction-Powered Communication with Distortion Guarantees |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2509.24373 |