Distributed Integrated Sensing and Edge AI Exploiting Prior Information
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913169180459008 |
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| author | Dong, Biao Cao, Bin Gui, Guan Zhang, Qinyu |
| author_facet | Dong, Biao Cao, Bin Gui, Guan Zhang, Qinyu |
| contents | This paper investigates a distributed ISEA system under a Bayesian framework, focusing on incorporating task-relevant priors to maximize inference performance. At the sensing level, an RWB estimator with a GM prior is designed. By weighting class-conditional posterior means with responsibilities, RWB effectively denoises features and outperforms ML at low SNR. At the communication level, two theoretical proxies are introduced: the computation-optimal and decision-optimal proxies. Optimal transceiver designs in terms of closed-form power allocation are derived for both TDM and FDM settings, revealing threshold-based and dual-decomposition structures. Results show that the discriminant-aware allocation yields additional inference gains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Integrated Sensing and Edge AI Exploiting Prior Information Dong, Biao Cao, Bin Gui, Guan Zhang, Qinyu Signal Processing This paper investigates a distributed ISEA system under a Bayesian framework, focusing on incorporating task-relevant priors to maximize inference performance. At the sensing level, an RWB estimator with a GM prior is designed. By weighting class-conditional posterior means with responsibilities, RWB effectively denoises features and outperforms ML at low SNR. At the communication level, two theoretical proxies are introduced: the computation-optimal and decision-optimal proxies. Optimal transceiver designs in terms of closed-form power allocation are derived for both TDM and FDM settings, revealing threshold-based and dual-decomposition structures. Results show that the discriminant-aware allocation yields additional inference gains. |
| title | Distributed Integrated Sensing and Edge AI Exploiting Prior Information |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.00309 |