Firefly Algorithm for Movable Antenna Arrays

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA’s effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

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Publication Details

Journal
IEEE Wireless Communications Letters
Published
2024-09-10
DOI
https://doi.org/10.1109/lwc.2024.3456899
Citations
8
Primary Topic
Antenna Design and Optimization
Type
article
Field-Weighted Citation Impact
10.39
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article

Firefly Algorithm for Movable Antenna Arrays

Derrick Wing Kwan Ng, Xin‐She Yang, Mạnh Kha Hoàng, Thuc Kieu-Xuan et al.
8 citations
IEEE Wireless Communications Letters
Antenna Design and Optimization
10.39
article

Firefly Algorithm for Movable Antenna Arrays

Derrick Wing Kwan Ng, Xin‐She Yang, Mạnh Kha Hoàng, Thuc Kieu-Xuan, Tong Van Luyen, Tuan Anh Le
article en
8 citations

Abstract

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA’s effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

IEEE Wireless Communications LettersVol. 13(11)
UNSW Sydney (AU), Hanoi University of Industry (VN), Middlesex University London (GB)
Affordable and clean energy
Openalex Percentile: Top 2%
Antenna Design and Optimization
10.39
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