eess.SP · 2026-09-10 · No. 111

Signal Processing, 2026-09-10.

4 new papers in eess.SP. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

    Ian C. Guzmán, Radu Babiceanu, Berker Peköz

    eess.SP · cs.LG · eess.SY

    More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardware-aware deep learning framework for multiclass detection of electrical faults and power quality disturbances in a 400 Hz aerospace power system. A high-fidelity simulation model inspired by the Boeing 787...

    arxiv.org/abs/2609.10479 · PDF

  2. 02

    Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

    Beier Li, Mai Vu

    eess.SP · cs.LG · cs.NI

    6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths amplify the impact of beam squinting. Traditional solutions, such as adding a true-time-delay filter to each antenna, are cost-prohibitive due to the required hardware scale. This paper proposes a signal processing alternative using Graph Neural Networks (GNNs) to optimize hybrid beamforming in...

    arxiv.org/abs/2609.09708 · PDF

  3. 03

    Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

    Zhuodong Liu, Xiangyu Li, Chunhong Yuan, Hongyang Du, Bodong Shang, Qingqing Wu, Tony Q. S. Quek, Mohsen Guizani

    eess.SP · cs.AI

    Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and distributed sensing. Vision-language-action (VLA) models offer a foundation by integrating visual perception, language understanding, and action generation into a unified closed-loop policy. However, training and adapting VLA...

    arxiv.org/abs/2609.09591 · PDF

  4. 04

    Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning

    Parsa Rajabi, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi, Halim Yanikomeroglu

    eess.SP · cs.AI · cs.CR · cs.LG · eess.SY

    Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly to unseen environments. This paper proposes a secure adaptive framework for authentication in multi-zone networks (SAFA-MZ), a causal...

    arxiv.org/abs/2609.09511 · PDF

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