Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non- orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    The Application of Machine Learning in mmWave-NOMA Systems


    Beteiligte:
    Cui, Jingjing (Autor:in) / Ding, Zhiguo (Autor:in) / Fan, Pingzhi (Autor:in)


    Erscheinungsdatum :

    2018-06-01


    Format / Umfang :

    164801 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Combining NOMA and mmWave Technology for Cellular Communication

    Naqvi, Syed Ahsan Raza / Hassan, Syed Ali | IEEE | 2016


    Random User Pairing in Massive-MIMO-NOMA Transmission Systems Based on mmWave

    Aghdam, Mohammad Reza Ghavidel / Abdolee, Reza / Azhiri, Fatemeh Asghari et al. | IEEE | 2018


    Energy Efficient mmWave NOMA Downlink Multi-Relay System for ITSN

    He, Yizhi / Jiao, Jian / Chen, Zeqiong et al. | IEEE | 2020