Extremum-Based Joint Compression and Detection for Distributed Sensing
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917366412083200 |
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| author | Weiss, Amir Lancho, Alejandro |
| author_facet | Weiss, Amir Lancho, Alejandro |
| contents | We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27733 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Extremum-Based Joint Compression and Detection for Distributed Sensing Weiss, Amir Lancho, Alejandro Signal Processing Information Theory We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decide whether their observations share a common underlying signal or are statistically independent. We propose a simple extremum-based strategy, in which the encoder sends the index of its largest sample and the decoder performs a scalar threshold test. We derive exact nonasymptotic false-alarm and misdetection probabilities and validate the analysis with representative simulations. |
| title | Extremum-Based Joint Compression and Detection for Distributed Sensing |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2603.27733 |