Water-filling (WF) is a well-established iterative solution to optimal power allocation in parallel fading channels. Slow iterative search can be impractical for allocating power to a large number of OFDM sub-channels. Neural networks (NN) can transform the iterative WF threshold search process into a direct high-dimensional mapping from channel gain to transmit power solution. Our results show that the NN can perform very well (error 0.05%) and can be shown to be indeed performing approximate WF power allocation. However, there is no guarantee on the NN is mapping between channel states and power output. Here, we attempt to explain the NN power allocation solution via the Meijer G-function as a general explainable symbolic mapping. Our early results indicate that whilst the Meijer G-function has universal representation potential, its large search space means finding the best symbolic representation is challenging.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Approximate Symbolic Explanation for Neural Network Enabled Water-Filling Power Allocation


    Beteiligte:


    Erscheinungsdatum :

    2020-05-01


    Format / Umfang :

    447571 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks

    Sultana, Ajmery / Zhao, Lian / Fernando, Xavier | IEEE | 2016


    Resource Allocation and User Association in Massive MIMO Enabled Wireless Backhaul Network

    Rajoria, Shweta / Trivedi, Aditya / Godfrey, W. Wilfred et al. | IEEE | 2019



    Uncertainty Propagation in Neural Network Enabled Multi-Channel Optimisation

    Li, Chen / Sun, Schyler C. / Al-Rubaye, Saba et al. | IEEE | 2020


    Joint Power and Time Allocation in NOMA-SWIPT Enabled Wireless Caching Networks

    Ren, Yuan / Qian, Kaiyue / Zhang, Xuewei et al. | IEEE | 2022