An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels
Abstract In massive multiple‐input multiple‐output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a...
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doaj-a1c58c4db57e472ca1b486088b3960fb2021-07-07T12:44:49ZengWileyIET Communications1751-86281751-86362021-07-0115121632164110.1049/cmu2.12176An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channelsZufan Zhang0Di Zhang1Xiaoqin Yan2Chenquan Gan3Qingyi Zhu4School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing ChinaSchool of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing ChinaSchool of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing ChinaSchool of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing ChinaSchool of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing ChinaAbstract In massive multiple‐input multiple‐output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a message passing detection (MPD) algorithm with a convolutional neural network (CNN) (denoted as iterative MPD‐CNN structure) under correlated noise channels, which is helpful to solve the issue of detection performance degradation in non‐ideal AWGN channels. Firstly, the MPD algorithm based on the channel hardening phenomenon is used to initially estimate the transmitted signal, and then the CNN is concatenated to remove the estimation error for obtaining more accurate channel noise, which provides a beneficial noise distribution for the MPD algorithm. Finally, the theoretical analysis and simulation results show that the proposed iterative MPD‐CNN structure can improve the detection performance in conditions of correlated noise channels and fewer antennas. Compared with the traditional MPD algorithm, its detection performance is more superior.https://doi.org/10.1049/cmu2.12176 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zufan Zhang Di Zhang Xiaoqin Yan Chenquan Gan Qingyi Zhu |
spellingShingle |
Zufan Zhang Di Zhang Xiaoqin Yan Chenquan Gan Qingyi Zhu An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels IET Communications |
author_facet |
Zufan Zhang Di Zhang Xiaoqin Yan Chenquan Gan Qingyi Zhu |
author_sort |
Zufan Zhang |
title |
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels |
title_short |
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels |
title_full |
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels |
title_fullStr |
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels |
title_full_unstemmed |
An iterative MPD‐CNN structure for massive MIMO detection under correlated noise channels |
title_sort |
iterative mpd‐cnn structure for massive mimo detection under correlated noise channels |
publisher |
Wiley |
series |
IET Communications |
issn |
1751-8628 1751-8636 |
publishDate |
2021-07-01 |
description |
Abstract In massive multiple‐input multiple‐output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a message passing detection (MPD) algorithm with a convolutional neural network (CNN) (denoted as iterative MPD‐CNN structure) under correlated noise channels, which is helpful to solve the issue of detection performance degradation in non‐ideal AWGN channels. Firstly, the MPD algorithm based on the channel hardening phenomenon is used to initially estimate the transmitted signal, and then the CNN is concatenated to remove the estimation error for obtaining more accurate channel noise, which provides a beneficial noise distribution for the MPD algorithm. Finally, the theoretical analysis and simulation results show that the proposed iterative MPD‐CNN structure can improve the detection performance in conditions of correlated noise channels and fewer antennas. Compared with the traditional MPD algorithm, its detection performance is more superior. |
url |
https://doi.org/10.1049/cmu2.12176 |
work_keys_str_mv |
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