Neural-Network-Based Digital Predistortion for Active Antenna Arrays under Load Modulation

Alberto Brihuega, Lauri Anttila, Mikko Valkama

Research output: Contribution to journalArticleScientificpeer-review

33 Downloads (Pure)


In this letter, we propose an efficient solution to linearize mmWave active antenna array transmitters that suffer from beam-dependent load modulation. We consider a dense neural network that is capable of modeling the correlation between the nonlinear distortion characteristics among different beams. This allows providing consistently good linearization regardless of the beamforming direction, thus avoiding the necessity of executing continuous digital predistortion parameter learning. The proposed solution is validated, conforming to 5G new radio transmit signal quality requirements, with extensive over-the-air RF measurements utilizing a state-of-the-art 64-element active antenna array operating at 28-GHz carrier frequency.

Original languageEnglish
Pages (from-to)843-846
Number of pages4
JournalIEEE Microwave and Wireless Components Letters
Issue number8
Publication statusPublished - 1 Aug 2020
Publication typeA1 Journal article-refereed


  • 5G new radio (NR)
  • digital predistortion (DPD)
  • load modulation
  • mmWave
  • neural networks (NNs)
  • nonlinear distortion

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Neural-Network-Based Digital Predistortion for Active Antenna Arrays under Load Modulation'. Together they form a unique fingerprint.

Cite this