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arXiv · 2512.00309

Distributed Integrated Sensing and Edge AI Exploiting Prior Information

Biao Dong·Bin Cao·Guan Gui·Qinyu Zhang
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Abstract

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.

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Biao Dong, Bin Cao, Guan Gui, Qinyu Zhang. 2026-07-26. Distributed Integrated Sensing and Edge AI Exploiting Prior Information. https://arxiv.org/abs/2512.00309

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