Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems

Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb

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

Abstract

As a promising candidate for future wireless systems, large intelligent surfaces (LISs) recently emerged to serve considerate improvements in both spectral and energy efficiencies. These surfaces consist of large numbers of passive elements capable of intelligently reflecting the incident signals. Since the LIS employs passive elements, critical challenges are inherent in the channel training/estimation process in order to properly design the LIS reflection matrices. One challenge particularly is how to acquire the channel knowledge with low training overhead and power consumption solutions. In this paper, we first propose an energy-efficient novel LIS architecture where all the LIS elements are passive except few non-uniformly distributed active elements (connected to the baseband). Then, we develop an efficient solution to design the LIS reflection matrices, with negligible training overhead, leveraging deep learning tools. Given what we call environment descriptors, the LIS has the ability to learn the optimal LIS reflection matrices. The simulation results show that the developed solution can approach the optimal upper bound, when only a small fraction of the LIS elements are active, yielding a promising solution for LIS systems from both energy efficiency and training overhead perspectives.

Original languageEnglish (US)
Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109626
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: Dec 9 2019Dec 13 2019

Publication series

Name2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

Conference

Conference2019 IEEE Global Communications Conference, GLOBECOM 2019
CountryUnited States
CityWaikoloa
Period12/9/1912/13/19

Keywords

  • Beamforming
  • Deep learning
  • Large intelligent surface
  • Millimeter wave
  • Smart reflect-array

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Health Informatics

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  • Cite this

    Taha, A., Alrabeiah, M., & Alkhateeb, A. (2019). Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems. In 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings [9013256] (2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM38437.2019.9013256