TransNet: Full Attention Network for CSI Feedback in FDD Massive MIMO System

Channel state information (CSI) is a key aspect of massive multi-input multi-output (MIMO) system. It depicts important properties of transmission channels such as scattering, fading, the attenuation of power with distance, etc. The quality and cost of CSI feedback between user equipment (UE) and ba...

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Bibliographic Details
Published inIEEE wireless communications letters Vol. 11; no. 5; pp. 903 - 907
Main Authors Cui, Yaodong, Guo, Aihuang, Song, Chunlin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Communications systems
Computer architecture
Costs
CSI feedback
Decoding
deep learning
Downlink
Feedback
Frequency division duplexing
inception network
Massive MIMO
MIMO communication
transformer architecture
Transformers
Uplink
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Summary:Channel state information (CSI) is a key aspect of massive multi-input multi-output (MIMO) system. It depicts important properties of transmission channels such as scattering, fading, the attenuation of power with distance, etc. The quality and cost of CSI feedback between user equipment (UE) and base station (BS) play vital roles in the quality of the whole communication system. In this letter, a new deep learning (DL) method based on Google's famous Transformer architecture is presented for CSI feedback in frequency division duplex (FDD) massive MIMO system. Simulation results show that the presented inception network named TransNet outperforms other DL methods on the quality of CSI feedback.
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3149416