Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming

Haya Al Kassir; Zaharias D. Zaharis; Pavlos I. Lazaridis; Nikolaos V. Kantartzis; Traianos V. Yioultsis; Ioannis P. Chochliouros; Thomas D. Xenos

doi:10.1109/BlackSeaCom54372.2022.9858214

Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming

Haya Al Kassir, Zaharias D. Zaharis, Pavlos I. Lazaridis, Nikolaos V. Kantartzis, Traianos V. Yioultsis, Ioannis P. Chochliouros, Thomas D. Xenos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

A comparison of various neural network (NN) architectures is performed in this paper in order to be used as beamformers applied to a linear antenna array composed of 16 microstrip elements. Two recurrent NNs using respectively gated recurrent units and long short-Term memory, a convolutional NN, and a feed-forward NN are used here as adaptive beamformers. Three cases are investigated, each one with a different number of incoming signals received by the antenna array, and the performance of each NN structure is evaluated using various metrics. The simulation results demonstrate the effectiveness of the deep learning-based beamformers in real-Time calculation of the optimal antenna array weights, while considering ever-changing environments.

Original language	English
Title of host publication	2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	282-287
Number of pages	6
ISBN (Electronic)	9781665497497
ISBN (Print)	9781665497503
DOIs	https://doi.org/10.1109/BlackSeaCom54372.2022.9858214
Publication status	Published - 24 Aug 2022
Event	2022 IEEE International Black Sea Conference on Communications and Networking - Sofia, Bulgaria Duration: 6 Jun 2022 → 9 Jun 2022

Conference

Conference	2022 IEEE International Black Sea Conference on Communications and Networking
Abbreviated title	BlackSeaCom 2022
Country/Territory	Bulgaria
City	Sofia
Period	6/06/22 → 9/06/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BlackSeaCom54372.2022.9858214Licence: Unspecified

Cite this

Kassir, H. A., Zaharis, Z. D., Lazaridis, P. I., Kantartzis, N. V., Yioultsis, T. V., Chochliouros, I. P., & Xenos, T. D. (2022). Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming. In 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022 (pp. 282-287). [9858214] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BlackSeaCom54372.2022.9858214

@inproceedings{a26a421c35484048a4136edd30ff0ded,

title = "Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming",

abstract = "A comparison of various neural network (NN) architectures is performed in this paper in order to be used as beamformers applied to a linear antenna array composed of 16 microstrip elements. Two recurrent NNs using respectively gated recurrent units and long short-Term memory, a convolutional NN, and a feed-forward NN are used here as adaptive beamformers. Three cases are investigated, each one with a different number of incoming signals received by the antenna array, and the performance of each NN structure is evaluated using various metrics. The simulation results demonstrate the effectiveness of the deep learning-based beamformers in real-Time calculation of the optimal antenna array weights, while considering ever-changing environments. ",

keywords = "Adaptive beamforming, beamforming, convolutional neural network (CNN), feed-forward neural network (FFNN), gated recurrent unit (GRU), long short-Term memory (LSTM), neural network, recurrent neural network (RNN)",

author = "Kassir, {Haya Al} and Zaharis, {Zaharias D.} and Lazaridis, {Pavlos I.} and Kantartzis, {Nikolaos V.} and Yioultsis, {Traianos V.} and Chochliouros, {Ioannis P.} and Xenos, {Thomas D.}",

note = "Funding Information: VII. ACKNOWLEDGMENTS This research was supported by the European Union, through the Horizon 2020 Marie Sk{\l}odowska-Curie Innovative Training Networks Programme “Mobility and Training for beyond 5G Ecosystems (MOTOR5G)” under grant agreement no. 861219. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022 ; Conference date: 06-06-2022 Through 09-06-2022",

year = "2022",

month = aug,

day = "24",

doi = "10.1109/BlackSeaCom54372.2022.9858214",

language = "English",

isbn = "9781665497503",

pages = "282--287",

booktitle = "2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Kassir, HA, Zaharis, ZD, Lazaridis, PI, Kantartzis, NV, Yioultsis, TV, Chochliouros, IP & Xenos, TD 2022, Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming. in 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022., 9858214, Institute of Electrical and Electronics Engineers Inc., pp. 282-287, 2022 IEEE International Black Sea Conference on Communications and Networking, Sofia, Bulgaria, 6/06/22. https://doi.org/10.1109/BlackSeaCom54372.2022.9858214

Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming. / Kassir, Haya Al; Zaharis, Zaharias D.; Lazaridis, Pavlos I. et al.

2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 282-287 9858214.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming

AU - Kassir, Haya Al

AU - Zaharis, Zaharias D.

AU - Lazaridis, Pavlos I.

AU - Kantartzis, Nikolaos V.

AU - Yioultsis, Traianos V.

AU - Chochliouros, Ioannis P.

AU - Xenos, Thomas D.

N1 - Funding Information: VII. ACKNOWLEDGMENTS This research was supported by the European Union, through the Horizon 2020 Marie Skłodowska-Curie Innovative Training Networks Programme “Mobility and Training for beyond 5G Ecosystems (MOTOR5G)” under grant agreement no. 861219. Publisher Copyright: © 2022 IEEE.

PY - 2022/8/24

Y1 - 2022/8/24

N2 - A comparison of various neural network (NN) architectures is performed in this paper in order to be used as beamformers applied to a linear antenna array composed of 16 microstrip elements. Two recurrent NNs using respectively gated recurrent units and long short-Term memory, a convolutional NN, and a feed-forward NN are used here as adaptive beamformers. Three cases are investigated, each one with a different number of incoming signals received by the antenna array, and the performance of each NN structure is evaluated using various metrics. The simulation results demonstrate the effectiveness of the deep learning-based beamformers in real-Time calculation of the optimal antenna array weights, while considering ever-changing environments.

AB - A comparison of various neural network (NN) architectures is performed in this paper in order to be used as beamformers applied to a linear antenna array composed of 16 microstrip elements. Two recurrent NNs using respectively gated recurrent units and long short-Term memory, a convolutional NN, and a feed-forward NN are used here as adaptive beamformers. Three cases are investigated, each one with a different number of incoming signals received by the antenna array, and the performance of each NN structure is evaluated using various metrics. The simulation results demonstrate the effectiveness of the deep learning-based beamformers in real-Time calculation of the optimal antenna array weights, while considering ever-changing environments.

KW - Adaptive beamforming

KW - beamforming

KW - convolutional neural network (CNN)

KW - feed-forward neural network (FFNN)

KW - gated recurrent unit (GRU)

KW - long short-Term memory (LSTM)

KW - neural network

KW - recurrent neural network (RNN)

UR - http://www.scopus.com/inward/record.url?scp=85137914831&partnerID=8YFLogxK

U2 - 10.1109/BlackSeaCom54372.2022.9858214

DO - 10.1109/BlackSeaCom54372.2022.9858214

M3 - Conference contribution

AN - SCOPUS:85137914831

SN - 9781665497503

SP - 282

EP - 287

BT - 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Black Sea Conference on Communications and Networking

Y2 - 6 June 2022 through 9 June 2022

ER -

Kassir HA, Zaharis ZD, Lazaridis PI, Kantartzis NV, Yioultsis TV, Chochliouros IP et al. Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming. In 2022 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 282-287. 9858214 doi: 10.1109/BlackSeaCom54372.2022.9858214

Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming

Abstract

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

MOTOR5G: H2020 ITN MOTOR5G : MObility and Training fOR beyond 5G Ecosystems

Cite this

Comparative Study of Neural Network Architectures Applied to Antenna Array Beamforming

Abstract

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

MOTOR5G: H2020 ITN MOTOR5G : MObility and Training fOR beyond 5G Ecosystems

Cite this