eess.SP · 2026-06-24 · No. 33

Signal Processing, 2026-06-24.

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

01 — The papers

3 entries
  1. 01

    Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN

    Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi, Mahnaz Arvaneh, Walid Saad, Hamed Ahmadi

    eess.SP · cs.AI

    The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel UAV trajectory optimization framework that...

    arxiv.org/abs/2606.24483 · PDF

  2. 02

    PROTECT-90: A Fault Dataset for Power System Protection

    Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann Jäger, Siming Bayer

    eess.SP · cs.LG

    The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable transparent and reproducible evaluation. To address this gap, this paper introduces the PROTECT-90 dataset, an open electromagnetic transient (EMT)-simulated reference benchmark for high-voltage fault studies with consistent digital-fault-recorder-like measurements,...

    arxiv.org/abs/2606.24298 · PDF

  3. 03

    Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation

    Saghar Bagheri, Gene Cheung, Tim Eadie, Antonio Ortega

    eess.SP · cs.LG

    A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising. For spatial-temporal data in which node-to-node similarities evolve over time, a static spatial graph is insufficient. In this paper, to represent slowly time-varying pairwise relationships, we model the graph...

    arxiv.org/abs/2606.24011 · PDF

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