Learn to Rapidly Optimize Hybrid Precoding.

Ortal Agiv, Nir Shlezinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hybrid precoding is expected to play a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost, size, and power. MIMO transmitters are required to frequently adapt their precoding patterns based on the variation in the channel conditions. In the hybrid setting, such an adaptation often involves lengthy optimization which may affect the network performance. In this work we employ the emerging learn-to-optimize paradigm to enable rapid optimization of hybrid precoders. In particular, we leverage data to learn iteration-dependent hyperparameter setting of projected gradient optimization, thus preserving the fully interpretable flow of the optimizer while improving its convergence speed. Numerical results demonstrate that our approach yields six to twelve times faster convergence compared to conventional optimization with shared hyperparameters, while achieving similar and even improved sum-rate performance.

Original languageEnglish
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781665494557
DOIs
StatePublished - 2022
Event23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022 - Oulu, Finland
Duration: 4 Jul 20226 Jul 2022

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2022-July

Conference

Conference23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Country/TerritoryFinland
CityOulu
Period4/07/226/07/22

Keywords

  • Hybrid MIMO
  • learn-to-optimize
  • precoding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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