Robust Continuous-Time Beam Tracking with Liquid Neural Network
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915264525762560 |
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| author | Zhu, Fenghao Wang, Xinquan Huang, Chongwen Jin, Richeng Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| author_facet | Zhu, Fenghao Wang, Xinquan Huang, Chongwen Jin, Richeng Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00365 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Continuous-Time Beam Tracking with Liquid Neural Network Zhu, Fenghao Wang, Xinquan Huang, Chongwen Jin, Richeng Yang, Qianqian Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane Information Theory Signal Processing 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. |
| title | Robust Continuous-Time Beam Tracking with Liquid Neural Network |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2405.00365 |