eess.SP · 2026-09-01 · No. 102

Signal Processing, 2026-09-01.

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

01 — The papers

2 entries
  1. 01

    Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI

    Sina Tavakolian, Abolfazl Zakeri, Ahmed Alkhateeb, Markku Juntti, Nhan Thanh Nguyen

    eess.SP · cs.LG

    Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead. This paper shows that fully digital cell-free mmWave beamforming can be effectively learned from sub-6 GHz CSI using a graph neural network (GNN). Specifically, we represent a CFmMIMO system as a wireless graph, and the GNN...

    arxiv.org/abs/2608.30524 · PDF

  2. 02

    Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction

    Ce Ju, Antoine Collas, Florent Bouchard, Bertrand Thirion

    eess.SP · cs.LG

    Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction, but strong performance within one cohort may not generalize to a new cohort. We ask whether within-dataset performance remains when the test data come from an entirely held-out rs-fMRI dataset. Each scan is represented as a regularized symmetric positive definite (SPD) correlation connectome,...

    arxiv.org/abs/2608.30418 · PDF

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