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arXiv · 2405.00365

Robust Continuous-Time Beam Tracking with Liquid Neural Network

Fenghao Zhu·Xinquan Wang·Chongwen Huang·Richeng Jin·Qianqian Yang·Ahmed Alhammadi·Zhaoyang Zhang·Chau Yuen·Mérouane Debbah
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Abstract

Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.

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Fenghao Zhu, Xinquan Wang, Chongwen Huang, Richeng Jin, Qianqian Yang, Ahmed Alhammadi, Zhaoyang Zhang, Chau Yuen, Mérouane Debbah. 2024-05-01. Robust Continuous-Time Beam Tracking with Liquid Neural Network. https://doi.org/10.1109/globecom52923.2024.10900942

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