Deep Learning for Super-Resolution DOA Estimation in Massive MIMO Systems

Hongji Huang, Guan Gui, Hikmet Sari, Fumiyuki Adachi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    13 Citations (Scopus)


    The requirement of the increasing capacity of the communication networks promotes the massive multiple input multiple output (MIMO), which has attracted a lot of attention among academic and industry communities. Due to the inherent sparsity features of channel structure in uplink massive MIMO systems, conventional methods often bring about high computational complexity and also fail to make full use of the structural information. In order to solve this problem, this paper proposes a novel deep learning (DL) based super-resolution direction of arrivals (DOA) estimation method. Specifically, it is realized with the aids of the well-designed deep neural network (DNN). Then we employ the DNN to carry out offline learning and online deployment procedures. This learning mechanism can learn the features of the wireless channel and the spacial structures efficiently. Finally, simulation results are provided to show that the proposed DL based scheme can achieve better performance in terms of the DOA estimation compared with conventional methods.

    Original languageEnglish
    Title of host publication2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538663585
    Publication statusPublished - 2018 Jul 2
    Event88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States
    Duration: 2018 Aug 272018 Aug 30

    Publication series

    NameIEEE Vehicular Technology Conference
    ISSN (Print)1550-2252


    Conference88th IEEE Vehicular Technology Conference, VTC-Fall 2018
    Country/TerritoryUnited States


    • DOA estimation
    • Massive multiple input multiple output (MIMO)
    • deep learning
    • learning policy

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering
    • Applied Mathematics


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