  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.
