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Volume 26
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Hierarchical MAB Framework for Energy-Aware Beam Training for Near-Field Communications

by
Yunxing Xiang
1,
Yi Yan
1,
Yunchao Song
2,*,
Jing Gao
1,
Xiaohui You
1,
Jun Wang
1,
Huibin Liang
2 and
Yixin Jiang
2
1
Supply Service Management Center, State Grid Jiangxi Electric Power Co. Ltd., Nanchang 330001, China
2
College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommuncations, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Sensors 2026, 26(1), 60; https://doi.org/10.3390/s26010060 (registering DOI)
Submission received: 10 November 2025 / Revised: 5 December 2025 / Accepted: 18 December 2025 / Published: 21 December 2025
(This article belongs to the Special Issue Applications of Wireless Communication Network Based on MIMO in Sensors)
Review Reports Versions Notes

Abstract

For XL-MIMO multi-user frequency division duplex systems, this paper proposes a near-field beam training scheme using a two-phase combinatorial multi-armed bandit (MAB) framework. This scheme leverages the MAB framework, integrating energy-aware user scheduling and hierarchical beam training to balance communication quality and device battery level, thereby effectively enhancing system energy efficiency and extending the device’s lifespan. Specifically, in the first phase, we account for user battery levels by designing an energy-aware upper confidence bound (UCB) algorithm for user scheduling. This algorithm effectively balances exploration and exploitation, prioritizing users with higher achievable rates and sufficient battery level. In the second phase, based on the scheduled users, two UCB algorithms are employed for beam training. In the first layer, discrete Fourier transform codebook-based beam scanning is utilized, and a UCB algorithm is applied to initially acquire angle information for scheduled users. In the second layer, based on the obtained angle information, a candidate set of polar-domain codewords is constructed. Another UCB algorithm is then employed to select the optimal polar-domain codewords. The effectiveness of our scheme is confirmed by simulations, demonstrating notable achievable rate gains for multi-user communications.
Keywords: XL-MIMO; beam training; user scheduling; MAB; energy efficiency; KL-UCB XL-MIMO; beam training; user scheduling; MAB; energy efficiency; KL-UCB

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MDPI and ACS Style

Xiang, Y.; Yan, Y.; Song, Y.; Gao, J.; You, X.; Wang, J.; Liang, H.; Jiang, Y. Hierarchical MAB Framework for Energy-Aware Beam Training for Near-Field Communications. Sensors 2026, 26, 60. https://doi.org/10.3390/s26010060

AMA Style

Xiang Y, Yan Y, Song Y, Gao J, You X, Wang J, Liang H, Jiang Y. Hierarchical MAB Framework for Energy-Aware Beam Training for Near-Field Communications. Sensors. 2026; 26(1):60. https://doi.org/10.3390/s26010060

Chicago/Turabian Style

Xiang, Yunxing, Yi Yan, Yunchao Song, Jing Gao, Xiaohui You, Jun Wang, Huibin Liang, and Yixin Jiang. 2026. "Hierarchical MAB Framework for Energy-Aware Beam Training for Near-Field Communications" Sensors 26, no. 1: 60. https://doi.org/10.3390/s26010060

APA Style

Xiang, Y., Yan, Y., Song, Y., Gao, J., You, X., Wang, J., Liang, H., & Jiang, Y. (2026). Hierarchical MAB Framework for Energy-Aware Beam Training for Near-Field Communications. Sensors, 26(1), 60. https://doi.org/10.3390/s26010060

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