arXiv AI
IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing
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arXiv Machine Learning
Sep 4
An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
The paper introduces a novel zero‑shot anomaly detection framework for multivariate IoT traffic data that combines adversarial learning and contrastive loss within a sequence‑based Variational Autoencoder. It achieves domain‑invariant latent representations and semantically structured embeddings without labeled data, using encoder/decoder adaptor layers to align feature distributions and a destination‑based segmentation strategy to model real‑world communication patterns. The method is evaluated on six diverse datasets across 44 transfer scenarios, showing strong zero‑shot generalization and competitive performance against a contrastive domain‑adaptation baseline in heterogeneous, privacy‑constrained IoT environments.
arXiv AI
Aug 18
A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
arXiv AI
Jun 3
WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
arXiv AI
Jul 21
Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
arXiv AI
Sep 7
A Deep Generative Model for Synthesizing Labeled Wireless Signals
The paper introduces Inter-Instance Generative Adversarial Networks (IIns‑GAN), a deep learning approach for synthesizing realistic labeled wireless signals. Unlike traditional environmental‑model based methods, IIns‑GAN adapts to various scenarios and produces signals that closely match the physical characteristics of real measurements. Experiments on public Ultra‑Wideband datasets show that the generated signals improve model training for tasks such as distance estimation and environment identification.
arXiv Computer Vision
Sep 22
CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing
CIG-MAE is a self‑supervised framework for WiFi‑based human action recognition that uses a cross‑modal masked autoencoder to reconstruct both amplitude and phase of Channel State Information. It introduces an adaptive, information‑guided masking strategy that focuses on high‑density time‑frequency regions and employs a Barlow Twins regularizer to align cross‑modal representations without negative samples. Experiments on three public datasets show that CIG‑MAE outperforms state‑of‑the‑art SSL methods and even surpasses a fully supervised baseline, highlighting its data efficiency, robustness, and generalization.