Tuning of Deep Neural Networks for Over-The-Air Linearization of Highly Nonlinear Wide-Band Active Phased Arrays

Feridoon Jalili*, Yufeng Zhang, Felice Francesco Tafuri, Ole Kiel Jensen, Yunfeng Li, Qingyue Chen, Ming Shen, Gert Frølund Pedersen

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

In this paper, we demonstrate how a deep neural network (DNN) can be used to compensate for nonlinearities and distortion effects introduced by the latest technology of 5G transmitters. A linearization approach based on neural networks can successfully cope with the challenges introduced not only by the high nonlinearity, wide bandwidth, and high frequency but also with challenges due to inter-PA crosstalk and load modulation. The device-under-test used in this experiment, is a state-of-the-art 5G 4x4 active phased array (APA) operating in very highly nonlinear regions at 28 GHz with a 100 MHz wide 3GPPbasestationsignalandwithOTAmeasuredsignalsusedfor training. Using the proposed DNN based linearization technique, an improvement of 11 % in error vector magnitude (EVM) and 10 dB adjacent channel leakage ratio (ACLR) are achieved which demonstrates the promising potential of this technique for emerging broadband communication systems such as 5G/6G and low earth orbit (LEO) satellite networks
Original languageEnglish
Title of host publication2021 International Symposium on Networks, Computers and Communications (ISNCC)
Number of pages4
PublisherIEEE
Publication date10 Jun 2021
ISBN (Print)978-1-6654-4829-1
ISBN (Electronic)978-1-6654-0304-7
DOIs
Publication statusPublished - 10 Jun 2021
Event2021 International Symposium on Networks, Computers and Communications (ISNCC) - Dubai, United Arab Emirates
Duration: 31 Oct 20212 Nov 2021

Conference

Conference2021 International Symposium on Networks, Computers and Communications (ISNCC)
Country/TerritoryUnited Arab Emirates
CityDubai
Period31/10/202102/11/2021

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