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.
From: Biao Dong [view email]
[v1]
Sat, 29 Nov 2025 04:05:53 UTC (1,048 KB)
[v2]
Wed, 20 May 2026 10:19:08 UTC (1 KB) (withdrawn)
[v3]
Wed, 27 May 2026 04:54:02 UTC (1 KB) (withdrawn)
[v4]
Thu, 28 May 2026 09:37:01 UTC (1,668 KB)
[v5]
Sun, 26 Jul 2026 07:21:18 UTC (2,486 KB)
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