Robust Continuous-Time Beam Tracking with Liquid Neural Network
<|reference_start|>Robust Continuous-Time Beam Tracking with Liquid Neural Network: 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.<|reference_end|>
@article{zhu2024robust,
    title={Robust Continuous-Time Beam Tracking with Liquid Neural Network},
    author={Fenghao Zhu, Xinquan Wang, Chongwen Huang, Richeng Jin, Qianqian Yang,
  Ahmed Alhammadi, Zhaoyang Zhang, Chau Yuen, and M'erouane Debbah},
    journal={arXiv preprint arXiv:2405.00365},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00365},
    primaryClass={cs.IT eess.SP math.IT}
}