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Paper Abstract and Keywords
Presentation 2026-01-27 14:20
A Study on Enhancing the Robustness of Element-Spacing Control in Two-Element Adaptive Arrays Using Deep Learning
Junya Miura, Kenta Umebayashi (TUAT) SR2025-77
Abstract (in Japanese) (See Japanese page) 
(in English) In two-element spacing-controlled adaptive array antennas, it is known that interference suppression can be ef
fectively achieved even in the presence of two or more interfering waves by appropriately controlling the spacing between
the two antenna elements. Conventionally, methods have been studied in which the optimal element spacing is searched for
and controlled based on the directions of arrival and power information of the desired and interfering signals. On the other
hand, as a simpler approach to determining the element spacing, deep-learning-based methods that use the correlation matrix
of the received array signals as input have been proposed. While these methods are effective when the desired signal power
is sufficiently high, it has been pointed out that they face difficulties in appropriately controlling the element spacing under
equal-power conditions between the desired and interfering signals. In this paper, we construct a deep learning model that
takes not only the correlation matrix but also the estimated direction of arrival of the desired signal as input, and demonstrate
that appropriate element spacing control can be achieved even under equal-power conditions. Furthermore, we investigate
methods to improve robustness against uncertainty in the number of arriving waves in deep learning. Specifically, assuming
scenarios in which two or three interfering waves arrive, we evaluate the estimation performance when the composition ratio
of training data for each condition is varied. The results show that it is necessary to include training data for both the two-wave
and three-wave cases, and that for the three-wave condition, appropriate element spacing control cannot be achieved unless a
sufficiently large proportion of three-wave training data is provided compared to the two-wave case.
Keyword (in Japanese) (See Japanese page) 
(in English) Array antenna / Two spacing control / Deep learning / Beam forming / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 336, SR2025-77, pp. 67-74, Jan. 2026.
Paper # SR2025-77 
Date of Issue 2026-01-19 (SR) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SR2025-77

Conference Information
Committee SR  
Conference Date 2026-01-26 - 2026-01-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Yamagata Terrsa 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AI/machine learning, IoT, Distributed networking, general topics 
Paper Information
Registration To SR 
Conference Code 2026-01-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Enhancing the Robustness of Element-Spacing Control in Two-Element Adaptive Arrays Using Deep Learning 
Sub Title (in English)  
Keyword(1) Array antenna  
Keyword(2) Two spacing control  
Keyword(3) Deep learning  
Keyword(4) Beam forming  
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Keyword(6)  
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Keyword(8)  
1st Author's Name Junya Miura  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Kenta Umebayashi  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2026-01-27 14:20:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2025-77 
Volume (vol) vol.125 
Number (no) no.336 
Page pp.67-74 
#Pages 8 
Date of Issue 2026-01-19 (SR) 

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