Title: RF Fingerprint Identification with Generalized Class Discovery via Learning-Aided Vector Quantization First Speaker: Omer Hazan MSc candidate supervised by Nir Shlezinger Second Speaker: Raz Zohar Electrical engineering MSc candidate supervised by Nir Shlezinger Title: AI-Aided Remote Inference For Loalization.

First Speaker: Omer Hazan MSc candidate supervised by Nir Shlezinger Abstract: Radio frequency fingerprinting identification (RFFI) enables device authentication by leveraging unique hardware- induced imperfections in analog signals. While recent deep learning- based methods have shown promise for RFFI, they often target only a subset of tasks such as closed-set classification or open-set recog- nition, and do not support discovering new devices. In this work, we propose a unified and extensible framework for RFFI based on deep learning-aided vector quantization. Our method casts feature extraction as a vector quantization problem in a learned latent space, enabling all three RFFI tasks through a single architecture: classifi- cation is performed via codeword selection, detecting unseen devices is achieved through entropy-based rejection, and registering them is realized by dynamically expanding the codebook with new entries. Evaluations on LoRa signal datasets demonstrate that our method outperforms existing RFFI solutions in accuracy, while uniquely enabling scalable and unsupervised discovery of new devices. Index Terms—RF fingerprint identification, device discovery. Bio: Omer Hazan MSc candidate @ BGU, supervised by Nir Shlezinger Second Speaker: Raz Zohar Electrical engineering MSc candidate supervised by Nir Shlezinger Abstract: Remote localization from array measurements is central to radar, wireless sensing, and autonomous systems, but practical deployments often involve multiple spatially separated sensors that must transmit limited information to a remote processor. In this seminar, we present a task-oriented remote inference framework for direction-of-arrival estimation that combines deep learning with classical array signal processing. Each sensing device extracts compact subspace-oriented features from its local observations, enabling the remote server to fuse information across multiple arrays and recover source directions under communication constraints. The framework preserves the structure of interpretable subspace methods such as ESPRIT and MUSIC while improving robustness to challenging conditions, including coherent sources, low SNR, limited snapshots, array miscalibration, and partial sensor observations. Beyond producing point estimates, we introduce uncertainty extraction as a key component of remote localization, allowing the system to quantify the reliability of each estimated direction and support confidence-aware fusion across sensors. This approach bridges model-based signal processing and deep learning toward robust, interpretable, and communication-efficient localization in distributed sensing systems. Bio: Raz Zohar MSc candidate @ BGU, supervised by Nir Shlezinger