Sun, Q;
Wu, Y;
Wang, J;
Xu, C;
Wong, KK;
(2019)
CNN-based CSI acquisition for FDD massive MIMO with noisy feedback.
Electronics Letters
, 55
(17)
pp. 963-965.
10.1049/el.2019.1724.
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Abstract
In frequency-division-duplex (FDD) massive multiple-input multiple-output (MIMO) systems, noisy feedback is a constant challenge for the base station (BS) to acquire accurate downlink channel state information (CSI). In this Letter, the authors propose a convolutional neural network (CNN)-based approach to overcome this problem, which they refer to it as an anti-noise CSI acquisition network (ANCAN). Results demonstrate that ANCAN can reconstruct CSI more accurately than other emerging CSI acquisition methods in the presence of noisy feedback links.
| Type: | Article |
|---|---|
| Title: | CNN-based CSI acquisition for FDD massive MIMO with noisy feedback |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1049/el.2019.1724 |
| Publisher version: | https://doi.org/10.1049/el.2019.1724 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | convolutional neural nets, feedback, frequency division multiplexing, MIMO communication, telecommunication computing, wireless channels |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10080810 |
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