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

• Published in: IEEE wireless communications letters Vol. 11; no. 5; pp. 903 - 907
• Main Authors: Cui, Yaodong, Guo, Aihuang, Song, Chunlin
• Format: Journal Article
• Language: English
• 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
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2022.3149416

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.