eess.SP · 2026-09-17 · No. 116

Signal Processing, 2026-09-17.

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

    Stable Filters for Generative Modeling of Graph Signals

    Martin Schmidt, Gonzalo Mateos

    eess.SP · cs.LG · stat.ML

    Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While recent graph-aware Schrödinger bridge models incorporate topology information directly into their reference dynamics, it is unclear how perturbations of the graph propagate through these dynamics and affect the resulting generated distributions. In this paper, we analyze the structural stability...

    arxiv.org/abs/2609.18759 · PDF

  2. 02

    Learning Array Signal Topologies as Conditional Neural Manifolds

    Julian P. Merkofer, Vincent van de Schaft, Ruud J. G. van Sloun

    eess.SP · cs.LG

    Subspace methods such as multiple signal classification (MUSIC) achieve super-resolution direction of arrival (DoA) estimation by exploiting the orthogonality between the array manifold and the noise subspace of the measurements. Their accuracy therefore depends on the assumed manifold and degrades under model mismatch, while parameters not identifiable from the spatial manifold cannot be recovered. In this work, we propose the conditional...

    arxiv.org/abs/2609.18616 · PDF

  3. 03

    Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction

    Cristian J. Vaca-Rubio, Konstantinos Vandikas, Aneta Vulgarakis Feljan

    eess.SP · cs.AI · cs.LG

    Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconstructing the channel, the UE transmits a learned \emph{semantic embedding} optimized end-to-end for beam selection at the gNB. Comparing reconstruction-oriented feedback (CsiNet) against task-aware...

    arxiv.org/abs/2609.18368 · PDF

  4. 04

    Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking

    Gaofeng Dong, Vamsi Eyunni, Pragya Sharma, Kang Yang, Mani Srivastava

    eess.SP · cs.LG

    Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership, privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many...

    arxiv.org/abs/2609.18173 · PDF

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