LAUSR

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.