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 contributionpeer-review

18 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


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


Dive into the research topics of 'Deep Learning for Super-Resolution DOA Estimation in Massive MIMO Systems'. Together they form a unique fingerprint.

Cite this