Distributed Integrated Sensing and Edge AI Exploiting Prior Information

TL;DR

This paper presents a Bayesian framework for distributed sensing and edge AI that leverages prior information to improve inference, proposing novel estimators and transceiver designs with demonstrated performance gains.

Contribution

It introduces a Bayesian approach with a new RWB estimator and optimal transceiver designs for distributed sensing and edge AI systems.

Findings

RWB estimator with GM prior outperforms ML at low SNR

Optimal transceiver designs are derived for TDM and FDM

Discriminant-aware power allocation enhances inference performance

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.