Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation

Yu Chien Lin, Ta Sung Lee, Zhi Ding

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Accurate estimation of DL CSI is required to achieve high spectrum and energy efficiency in massive MIMO systems. Previous works have developed learning-based CSI feedback framework within FDD systems for efficient CSI encoding and recovery with demonstrated benefits. However, downlink pilots for CSI estimation by receiving terminals may occupy excessively large number of resource elements for massive number of antennas and compromise spectrum efficiency. To overcome this problem, we propose a new learning-based feedback architecture for efficient encoding of partial CSI feedback of interleaved non-overlapped antenna subarrays by exploiting CSI temporal correlation. For ease of encoding, we further design an IFFT approach to decouple partial CSI of antenna subarrays and to preserve partial CSI sparsity. Our results show superior performance in indoor/outdoor scenarios by the proposed model for CSI recovery at significantly reduced computation power and storage needs.

原文English
主出版物標題55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
編輯Michael B. Matthews
發行者IEEE Computer Society
頁面345-350
頁數6
ISBN(電子)9781665458283
DOIs
出版狀態Published - 2021
事件55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
持續時間: 31 10月 20213 11月 2021

出版系列

名字Conference Record - Asilomar Conference on Signals, Systems and Computers
2021-October
ISSN(列印)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
國家/地區United States
城市Virtual, Pacific Grove
期間31/10/213/11/21

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