• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Search-free precoder selection for 5G new radio using neural networks

      Thumbnail
      View / Download
      1.1 Mb
      Author(s)
      Akyıldız, Talha
      Duman, Tolga M.
      Date
      2020-12
      Source Title
      2020 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2020
      Publisher
      IEEE
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      70
      views
      485
      downloads
      Abstract
      We propose a search-free precoder selection method with neural networks motivated by the fact that large codebook sizes are adopted in 5G New Radio (5G-NR). The proposed method does not require an explicit codebook search unlike the traditional selection algorithms. Instead, it aims at finding the precoder matrix index that maximizes the corresponding channel capacity using a neural network directly. The network is trained off-line using extensive simulated data with the underlying channel statistics; however, the actual selection algorithm is based on simple calculations with the neural network, hence it is feasible for real time implementation. We demonstrate that the proposed search-free selection algorithm is highly efficient, i.e., it results in a performance very close to optimal precoder in the codebook while its complexity is significantly lower. Simulations with realistic channel models of 5G-NR corroborate these observations as well. We also show that pruning of the trained neural network gives a way to achieve further complexity reduction with a very small reduction in the system performance.
      Keywords
      5G New radio
      MIMO
      Channel state information
      Precoding
      Neural networks
      Permalink
      http://hdl.handle.net/11693/54996
      Published Version (Please cite this version)
      https://doi.org/10.1109/BlackSeaCom48709.2020.9234966
      Collections
      • Department of Electrical and Electronics Engineering 3868
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy