MIMO Channel Information Feedback Using Deep Recurrent Network

Lu, Chao, Xu, Wei, Shen, Hong, Zhu, Jun and Wang, Kezhi (2019) MIMO Channel Information Feedback Using Deep Recurrent Network. IEEE Communications Letters, 23 (1). pp. 188-191. ISSN 1089-7798

1811.07535.pdf - Accepted Version

Download (150kB) | Preview
Official URL: https://doi.org/10.1109/LCOMM.2018.2882829


In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network (NN) based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory (LSTM) which admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.

Item Type: Article
Uncontrolled Keywords: Channel state information (CSI) feedback, recurrent neural network (RNN), multiple-input multiple-output (MIMO)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 26 Nov 2018 09:40
Last Modified: 01 Aug 2021 07:32
URI: http://nrl.northumbria.ac.uk/id/eprint/36908

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics