Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge

Xinxin Liu; Qiang Hua; Yuanming Cai; Ke Li; Mobayode O. Akinsolu; Bo Liu; Lei Wang

doi:10.1109/iWAT66946.2026.11501894

Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge

Xinxin Liu, Qiang Hua, Yuanming Cai, Ke Li, Mobayode O. Akinsolu, Bo Liu, Lei Wang

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

Abstract

Leaky-wave antennas (LWAs) usually suffer from poor broadside radiation due to the open stopband (OSB) issue. In this paper, a folded leaky-wave antenna (FLWA) based on a substrate integrated waveguide (SIW) is investigated. In order to mitigate the OSB, an additional set of periodic slots is introduced in the middle-layer copper of the FLWA. Degrees of freedom on the top-layer radiating slots, middle-layer coupling slots and the folded SIW are all given to an in-house machine learning algorithm named Surrogate-model-Assisted Differential Evolution for Antenna Synthesis (SADEA-V). With no need for prior knowledge, an effective OSB suppressed FLWA has been successfully designed by the proposed machine learning-based approach. In details, the antenna operates over a frequency range from 23 GHz to 29.4 GHz, achieving a realized gain of 9.36 12.2 dBi in the main beam direction and a continuous beamscanning from −27.5∘ to +14∘.

Original language	English
Title of host publication	2026 International Workshop on Antenna Technology (IWAT)
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	9798331573034
ISBN (Print)	9798331573041
DOIs	https://doi.org/10.1109/iWAT66946.2026.11501894
Publication status	Published - 6 May 2026
Event	2026 International Workshop on Antenna Technology - VG&M, University of Liverpool, Liverpool, United Kingdom Duration: 25 Mar 2026 → 27 Mar 2026 https://attend.ieee.org/iwat-2026/

Publication series

Name	IEEE International Workshop on Antenna Technology
Publisher	IEEE
ISSN (Print)	2770-4262
ISSN (Electronic)	2770-4254

Conference

Conference	2026 International Workshop on Antenna Technology
Country/Territory	United Kingdom
City	Liverpool
Period	25/03/26 → 27/03/26
Internet address	https://attend.ieee.org/iwat-2026/

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Cite this

Liu, X., Hua, Q., Cai, Y., Li, K., Akinsolu, M. O., Liu, B., & Wang, L. (2026). Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge. In 2026 International Workshop on Antenna Technology (IWAT) Article 11501894 (IEEE International Workshop on Antenna Technology). IEEE. https://doi.org/10.1109/iWAT66946.2026.11501894

@inproceedings{f36cdae1ec3d4d398b3cdf6772331e1f,

title = "Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge",

abstract = "Leaky-wave antennas (LWAs) usually suffer from poor broadside radiation due to the open stopband (OSB) issue. In this paper, a folded leaky-wave antenna (FLWA) based on a substrate integrated waveguide (SIW) is investigated. In order to mitigate the OSB, an additional set of periodic slots is introduced in the middle-layer copper of the FLWA. Degrees of freedom on the top-layer radiating slots, middle-layer coupling slots and the folded SIW are all given to an in-house machine learning algorithm named Surrogate-model-Assisted Differential Evolution for Antenna Synthesis (SADEA-V). With no need for prior knowledge, an effective OSB suppressed FLWA has been successfully designed by the proposed machine learning-based approach. In details, the antenna operates over a frequency range from 23 GHz to 29.4 GHz, achieving a realized gain of 9.36 12.2 dBi in the main beam direction and a continuous beamscanning from −27.5∘ to +14∘.",

keywords = "Folded leaky-wave antenna (FLWA), millimeterwave (mmWave), open stopband (OSB) suppression, substrate integrated waveguide (SIW)",

author = "Xinxin Liu and Qiang Hua and Yuanming Cai and Ke Li and Akinsolu, \{Mobayode O.\} and Bo Liu and Lei Wang",

year = "2026",

month = may,

day = "6",

doi = "10.1109/iWAT66946.2026.11501894",

language = "English",

isbn = "9798331573041",

series = "IEEE International Workshop on Antenna Technology",

publisher = "IEEE",

booktitle = "2026 International Workshop on Antenna Technology (IWAT)",

address = "United States",

note = "2026 International Workshop on Antenna Technology ; Conference date: 25-03-2026 Through 27-03-2026",

url = "https://attend.ieee.org/iwat-2026/",

}

Liu, X, Hua, Q, Cai, Y, Li, K, Akinsolu, MO, Liu, B & Wang, L 2026, Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge. in 2026 International Workshop on Antenna Technology (IWAT)., 11501894, IEEE International Workshop on Antenna Technology, IEEE, 2026 International Workshop on Antenna Technology, Liverpool, United Kingdom, 25/03/26. https://doi.org/10.1109/iWAT66946.2026.11501894

Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge. / Liu, Xinxin; Hua, Qiang; Cai, Yuanming et al.
2026 International Workshop on Antenna Technology (IWAT). IEEE, 2026. 11501894 (IEEE International Workshop on Antenna Technology).

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

TY - GEN

T1 - Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge

AU - Liu, Xinxin

AU - Hua, Qiang

AU - Cai, Yuanming

AU - Li, Ke

AU - Akinsolu, Mobayode O.

AU - Liu, Bo

AU - Wang, Lei

PY - 2026/5/6

Y1 - 2026/5/6

N2 - Leaky-wave antennas (LWAs) usually suffer from poor broadside radiation due to the open stopband (OSB) issue. In this paper, a folded leaky-wave antenna (FLWA) based on a substrate integrated waveguide (SIW) is investigated. In order to mitigate the OSB, an additional set of periodic slots is introduced in the middle-layer copper of the FLWA. Degrees of freedom on the top-layer radiating slots, middle-layer coupling slots and the folded SIW are all given to an in-house machine learning algorithm named Surrogate-model-Assisted Differential Evolution for Antenna Synthesis (SADEA-V). With no need for prior knowledge, an effective OSB suppressed FLWA has been successfully designed by the proposed machine learning-based approach. In details, the antenna operates over a frequency range from 23 GHz to 29.4 GHz, achieving a realized gain of 9.36 12.2 dBi in the main beam direction and a continuous beamscanning from −27.5∘ to +14∘.

AB - Leaky-wave antennas (LWAs) usually suffer from poor broadside radiation due to the open stopband (OSB) issue. In this paper, a folded leaky-wave antenna (FLWA) based on a substrate integrated waveguide (SIW) is investigated. In order to mitigate the OSB, an additional set of periodic slots is introduced in the middle-layer copper of the FLWA. Degrees of freedom on the top-layer radiating slots, middle-layer coupling slots and the folded SIW are all given to an in-house machine learning algorithm named Surrogate-model-Assisted Differential Evolution for Antenna Synthesis (SADEA-V). With no need for prior knowledge, an effective OSB suppressed FLWA has been successfully designed by the proposed machine learning-based approach. In details, the antenna operates over a frequency range from 23 GHz to 29.4 GHz, achieving a realized gain of 9.36 12.2 dBi in the main beam direction and a continuous beamscanning from −27.5∘ to +14∘.

KW - Folded leaky-wave antenna (FLWA)

KW - millimeterwave (mmWave)

KW - open stopband (OSB) suppression

KW - substrate integrated waveguide (SIW)

UR - https://www.scopus.com/pages/publications/105040671918

U2 - 10.1109/iWAT66946.2026.11501894

DO - 10.1109/iWAT66946.2026.11501894

M3 - Conference contribution

SN - 9798331573041

T3 - IEEE International Workshop on Antenna Technology

BT - 2026 International Workshop on Antenna Technology (IWAT)

PB - IEEE

T2 - 2026 International Workshop on Antenna Technology

Y2 - 25 March 2026 through 27 March 2026

ER -

Open Stopband Suppressed Folded SIW Leaky-Wave Antenna Inspired by Machine Learning without Prior Knowledge

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