arXiv:2407. 13303v2 Announce Type: replace Abstract: Conventional large-scale indoor localization based on Wi-Fi RSSI fingerprinting faces issues of time-consuming and labor-intensive labeled data collection, limited generalization of a model trained under a supervised learning (SL) framework due to its inability to leverage unlabeled data, and model performance degradation in dynamic scenarios with environmental variations.
arXiv:2409. 00078v2 Announce Type: replace-cross Abstract: As a large number of Internet of Things (IoT) devices are deployed in the field, there arises huge potential of edge computing for indoor localization on those devices.
arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.
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:2606. 17462v1 Announce Type: new Abstract: While Website Fingerprinting (WF) attacks achieve high accuracy in controlled laboratory settings, they often degrade substantially in real-world environments due to spatio-temporal drift, browser heterogeneity, proxy obfuscation and etc.
AdaDim introduces a training strategy for self‑supervised learning that adaptively balances dimensionality increase and mutual information reduction. By gradually regularizing the projection head while encouraging feature decorrelation and sample uniformity, AdaDim achieves up to 3% performance gains over standard SSL baselines without relying on costly techniques such as queues or predictor networks. The method demonstrates that optimal SSL models do not simply maximize dimensionality or minimize mutual information, but find a trade‑off between the two.
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
arXiv:2601. 21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-SSL) have struggled to build functional representations in deep neural networks.
arXiv:2607. 03196v1 Announce Type: cross Abstract: WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices while preserving individual privacy concerns.
arXiv:2609.17204v1 Announce Type: cross Abstract: Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), colle...
arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.
arXiv:2609.06131v1 Announce Type: new Abstract: Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situationa...