期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 9, 期 9, 页码 1447-1451出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2020.2993699
关键词
Channel estimation; MIMO communication; Complexity theory; Training; Machine learning; Surface waves; Array signal processing; Deep learning; channel estimation; large intelligent surfaces; massive MIMO
资金
- ERC Project AGNOSTIC
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
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