Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design parameters such as inter-site distances, number of sectors per site, and the up-tilt angles of sector antennas. The system-level design guidelines in the existing work on A2G network are rather limited. In this paper, a novel deep learning-based framework is proposed for efficient design and optimization of a 5G A2G network. The devised architecture comprises two deep neural networks (DNNs): the first DNN is used for approximating the 5G A2G network behavior in terms of user throughput, and the second DNN is developed as a function optimizer to find the throughput-optimal deployment parameters including antenna up-tilt angles and inter-site distances. Simulation results are provided to validate the proposed model and reveal system-level design insights.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    5G Air-to-Ground Network Design and Optimization: A Deep Learning Approach


    Beteiligte:
    Chen, Yun (Autor:in) / Lin, Xingqin (Autor:in) / Khan, Talha (Autor:in) / Afshang, Mehrnaz (Autor:in) / Mozaffari, Mohammad (Autor:in)


    Erscheinungsdatum :

    2021-04-01


    Format / Umfang :

    2913513 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Road Network Energy Optimization Using IoT and Deep Learning

    Balamurugan, N. M. / Revathi, N. / Gayathri, R. | Wiley | 2022


    Energy Efficiency Optimization in LoRa Networks—A Deep Learning Approach

    Tu, Lam-Thanh / Bradai, Abbas / Ahmed, Olfa Ben et al. | IEEE | 2023