IEICE TRANSACTIONS on Information
Secret Key Generation Scheme Based on Deep Learning in FDD MIMO Systems
Summary :
In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.7 pp.1058-1062
- Publication Date
- 2021/07/01
- Publicized
- 2021/04/07
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDL8145
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Zheng WAN
Information Engineering University
Kaizhi HUANG
Information Engineering University
Lu CHEN
Information Engineering University
Keyword
Latest Issue
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Zheng WAN, Kaizhi HUANG, Lu CHEN, "Secret Key Generation Scheme Based on Deep Learning in FDD MIMO Systems" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 7, pp. 1058-1062, July 2021, doi: 10.1587/transinf.2020EDL8145.
Abstract: In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8145/_p
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@ARTICLE{e104-d_7_1058,
author={Zheng WAN, Kaizhi HUANG, Lu CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Secret Key Generation Scheme Based on Deep Learning in FDD MIMO Systems},
year={2021},
volume={E104-D},
number={7},
pages={1058-1062},
abstract={In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.},
keywords={},
doi={10.1587/transinf.2020EDL8145},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Secret Key Generation Scheme Based on Deep Learning in FDD MIMO Systems
T2 - IEICE TRANSACTIONS on Information
SP - 1058
EP - 1062
AU - Zheng WAN
AU - Kaizhi HUANG
AU - Lu CHEN
PY - 2021
DO - 10.1587/transinf.2020EDL8145
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2021
AB - In this paper, a deep learning-based secret key generation scheme is proposed for FDD multiple-input and multiple-output (MIMO) systems. We built an encoder-decoder based convolutional neural network to characterize the wireless environment to learn the mapping relationship between the uplink and downlink channel. The designed neural network can accurately predict the downlink channel state information based on the estimated uplink channel state information without any information feedback. Random secret keys can be generated from downlink channel responses predicted by the neural network. Simulation results show that deep learning based SKG scheme can achieve significant performance improvement in terms of the key agreement ratio and achievable secret key rate.
ER -
English
