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Transformer-based Efficient CSI Feedback for THz band FDD MIMO Systems
- Authors
- Ji, Dong Jin; Chung, Byung Chang
- Issue Date
- Feb-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- 6G mobile communication; Antenna arrays; channel feedback; Computational modeling; Computer architecture; deep learning; Machine learning for communications; multiple-input multiple-output; Receiving antennas; Tensors; Transformers
- Citation
- IEEE Wireless Communications Letters, v.13, no.2, pp 1 - 1
- Pages
- 1
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Wireless Communications Letters
- Volume
- 13
- Number
- 2
- Start Page
- 1
- End Page
- 1
- ISSN
- 2162-2337
2162-2345
- Abstract
- 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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