Robust Continuous-Time Beam Tracking with Liquid Neural Network

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.

6 pages, 6 figures. Accepted by IEEE Global Communications Conference (GLOBECOM) 2024

References in corpus (9)

• Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
• The Road Towards 6G: A Comprehensive Survey
• Closed-form Continuous-time Neural Models
• Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning
• Robust Beamforming with Gradient-based Liquid Neural Network
• Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning
• Beamforming Inferring by Conditional WGAN-GP for Holographic Antenna Arrays
• Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
• Multi-Sources Information Fusion Learning for Multi-Points NLOS Localization