ScholarWorks@경상국립대학교

초록

Machine learning algorithms have been extensively explored for the feedback of multiple-input multiple-output (MIMO) channel state information (CSI) in orthogonal frequency division multiplexing (OFDM) systems. However, their viability in sixth-generation (6G) wireless communication systems, operating in the terahertz (THz) band, remains uncertain. To address this, we propose ChannelTransformer, a transformer-model-based CSI feedback scheme that incorporates multi-head self-attention and a CSI-feedback-aware transformer structure, and a lightweight user equipment(UE) model. Through simulations in the DeepMIMO O1 scenario at 140GHz, ChannelTransformer demonstrates superior performance in terms of normalized mean square error (NMSE) and cosine similarity across various feedback lengths compared to conventional schemes with a much smaller UE model size. IEEE

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