{"ID":3053119,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T11:43:53.432517148Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04076","arxiv_id":"2606.04076","title":"SkySense: A Semi-Supervised Generative Framework for UAV Localization in ISAC Networks","abstract":"Extreme data scarcity and inherent multipath spatial ambiguity severely limit existing deep learning-based channel state information (CSI) fingerprinting localization schemes for target unmanned aerial vehicles (UAVs). To overcome these challenges, we propose an end-to-end semi-supervised generative localization framework. First, by exploiting the temporal correlations inherent in continuous flight trajectories, a self-supervised encoder extracts robust spatial features from massive unlabeled CSI sequences to establish structured latent representations. Following this, we utilize a consistency model, a powerful derivative of diffusion architectures, as the core generative backbone to map the learned latent space to physical coordinates, jointly fine-tuning the pre-trained encoder with a strictly limited set of labeled CSI. This consistency formulation models the conditional distribution to resolve the mean collapse problem of discriminative models, while compressing the inference trajectory to 1-2 steps to avoid the latency bottleneck of traditional diffusion models. Furthermore, a lightweight distributed fusion mechanism is designed to aggregate spatial predictions across multiple base stations (BS) from a multi-view geometry perspective. Comprehensive evaluations on a real-world measurement dataset demonstrate that our framework achieves low latency and suppresses the mean localization error to 9.77 cm under a 3-BS fusion setup with only a 1\\% label fraction, significantly outperforming existing fully supervised and semi-supervised discriminative baselines.","short_abstract":"Extreme data scarcity and inherent multipath spatial ambiguity severely limit existing deep learning-based channel state information (CSI) fingerprinting localization schemes for target unmanned aerial vehicles (UAVs). To overcome these challenges, we propose an end-to-end semi-supervised generative localization framew...","url_abs":"https://arxiv.org/abs/2606.04076","url_pdf":"https://arxiv.org/pdf/2606.04076v1","authors":"[\"Shenghan Luo\",\"Yin Xu\",\"Jie Yang\",\"Yang Wang\",\"Cixiao Zhang\",\"Shi Jin\",\"Wenjun Zhang\"]","published":"2026-06-02T17:06:58Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Diffusion Model\"]","has_code":false}