An Adaptive Beamforming Approach Applied to Planar Antenna Arrays Using Neural Networks

Ioannis Mallioras, Zaharias D. Zaharis, Pavlos I. Lazaridis, Vladimir Poulkov, Nikolaos V. Kantartzis, Traianos V. Yioultsis

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

1 Citation (Scopus)

Abstract

Future wireless networks depend on the improvement of current smart antenna operations so that they maintain high accuracy levels at low response times. Utilizing machine learning techniques, it is possible to replace the currently used algorithms with a much faster yet reliable alternative. In this study, we focus on adaptive beamforming applied to a planar antenna array using the null steering beamforming algorithm (NSB). We test different types of deep neural networks (DNNs) as potential alternative beamformers, by comparing their accuracy to that of the NSB algorithm. The application concerns an 8×8 planar antenna array composed of isotropic elements. The DNNs tested here are the traditional feedforward neural networks and recurrent neural networks using either gated recurrent units or long short-Term memory units. In addition, we investigate each DNN type to make sure we are utilizing the best version of each neural network architecture.

Original languageEnglish
Title of host publication2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages293-297
Number of pages5
ISBN (Electronic)9781665497497
ISBN (Print)9781665497503
DOIs
Publication statusPublished - 24 Aug 2022
Event2022 IEEE International Black Sea Conference on Communications and Networking - Sofia, Bulgaria
Duration: 6 Jun 20229 Jun 2022

Conference

Conference2022 IEEE International Black Sea Conference on Communications and Networking
Abbreviated titleBlackSeaCom 2022
Country/TerritoryBulgaria
CitySofia
Period6/06/229/06/22

Fingerprint

Dive into the research topics of 'An Adaptive Beamforming Approach Applied to Planar Antenna Arrays Using Neural Networks'. Together they form a unique fingerprint.

Cite this