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新论文 · v1arXiv:2611.02378

A Beam-Switched Sparse Circular Array for Passive Wi-Fi Sensing

Yang Chen, Riccardo Bersan, Daniele Piazza, Stefano Savazzi, Michele D'Amico

原文摘要

Wi-Fi sensing applications require unambiguous angle-of-arrival (AoA) estimation over the full azimuth plane. Uniform circular arrays (UCAs) are shown to remove this ambiguity, but deploying the four RF chains typically available on commercial Wi-Fi chipsets into a sparse layout suffers from spatial undersampling. Such undersampling produces strong grating lobes. To overcome this hardware bottleneck, we first establish a generalized array manifold incorporating element radiation patterns, revealing the suitability and degradation mechanisms of directional UCAs. Then we propose an angularly staggered beam-switched UCA architecture that leverages the spatial complementarity. Numerical evaluations demonstrate that the proposed scheme effectively mitigates grating-lobe ambiguities without increasing the number of RF chains, significantly reducing AoA estimation outliers and improving omnidirectional tracking performance.

eess.SP · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07860

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

Usevalad Milasheuski, Piero Baraldi, Enrico Zio, Stefano Savazzi

原文摘要

Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT) environments. While discriminative models dominate most FL use cases, recent advances in generative models - such as Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Diffusion Models (DM) - offer new opportunities for unsupervised anomaly detection in time series analysis, with relevant applications in predictive maintenance (PdM) in critical industrial infrastructures. In this work, we present a comprehensive analysis of VAEs, GANs, and DMs in the context of federated PdM. We analyze their performance and communication overhead under both full and partial federation setups, where only subsets of model components are shared. Building on split-parameter personalized FL, this paper provides an empirical instantiation of shared/private parameter partitioning for unsupervised generative Time-Series Anomaly Detection (TSAD), mapping the split to analysis and synthesis modules across the generative architectures. Our experiments over five clients and two industrial time-series benchmarks reveal distinct trade-offs in model utility, stability, and communication efficiency in heterogeneous cross-silo FL. For the evaluated GAN-based configurations, full federation improves final anomaly-detection utility relative to independent local training, although these models remain less robust than the VAE- and DDPM-based alternatives. For DMs, decoder sharing matches full federation while substantially reducing the communication payload.

cs.LG · cs.AI

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arXiv ↗PDF ↗