Eigenfunctions are widely used to characterize ker-nels in many data-driven analyses. In machine learning, eigen- function decomposition is primarily based on Mercer's theorem, which requires the kernel to be symmetric. This is difficult to satisfy in communication systems as the channel kernel is usually asymmetric due to the different downlink and uplink propagation environments. High Order Generalized Mercer's Theorem (HOGMT) provides a principled way to decompose any multi-dimensional asymmetric kernel into eigenfunctions. To manage the complexity of the eigen-decomposition, we propose an equivalent Neural Network (NN) for decomposing a gen-eral channel kernel. This is further improved by applying the Augmented Lagrangian Method (ALM) to reduce the training time and parameter tuning, which avoids additional tuning rounds when the size of the kernel or the number of eigen- components change depending on the wireless environment. We validate the adaptability of the proposed NN and its accu-racy using simulations in PyTorch. The code is available at https://github.com/ZBZou/HOGMT-ALM/tree/main.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Neural Network for Eigen-Decomposition of Multi-Dimensional Channel Kernels


    Beteiligte:
    Amarasekara, Iresha (Autor:in) / Zou, Zhibin (Autor:in) / Dutta, Aveek (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    807333 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    A Coupled Principal/Minor Eigen-pairs Extraction Neural Network Algorithm

    Wang, Ben / Kong, Xiangyu / Feng, Xiaowei et al. | British Library Online Contents | 2017


    Ambiguity Resolution Validation Based on LAMBDA and Eigen-Decomposition

    Tsai, Y.-F. / Juang, J.-C. / Institute of Navigation | British Library Conference Proceedings | 2007


    Deformation Decomposition Based on the Elemental Eigen-Deformation Shapes

    Wang, J.M. / Yuan, L.L. / Law, S.S. et al. | British Library Conference Proceedings | 2010