Multi-scenario time-domain channel extraploation: A Transformer-based approach

Time-domain channel prediction offers a promising solution for obtaining Channel State Information (CSI) in high-mobility communication systems, while minimizing overhead. However, current deep learning-based channel prediction models face significant challenges in generalization, often performing poorly when applied to scenarios different from their training data. To address this challenge, we propose a novel transformer-based time-domain channel prediction framework that generalizes effectively across multiple scenarios. Extensive simulations demonstrate that our proposed framework substantially outperforms conventional models in terms of generalization capability across various scenarios and signal-to-noise ratios (SNR). The models we compared include Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Bidirectional GRU (BiGRU), and standard Transformer architectures. Our results underscore the potential of this approach to significantly advance the field of channel prediction in dynamic communication environments.