Multi-Modal Beam Selection: A Transfer Methodology for Multi-Frequency

Dong Wang, Huiyang Wang, Mei Tu, Fan Zhang, Xiangfeng Gao, Zhigang Wang

Abstract: This paper investigates beam selection for multiple-frequency via deep learning. Existing learning-based beam selection methods are typically data-hungry to train the neural network. However, collecting sufficient data is a major challenge that hinders the generalization of networks. To address this challenge, this paper develops a frequency transfer method and proposes a multi-modal beam selection network, named FtransNet, which demonstrates superior generalization performance to different scenarios and carrier frequencies. Compared to the traditional approach of only transferring a model, we also transfer and augment high-quality samples to the new frequency, which significantly enlarges the environment features and path loss features in the dataset. Moreover, we embed the relative locations and the reflection features of the environment to assist the best beam selection, which further enhances the generalization of the network. Simulation results show that the proposed FtransNet outperforms the existing network with high beam selection accuracy.