Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems
Millimeter wave (mmWave) system tends to have a large number of antenna elements to compensate for the high channel path loss. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, two-step hybrid precoding algorithms that enabl...
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doaj-12b52c2fc1354f5abe6766455e0d06f62021-03-30T02:54:25ZengIEEEIEEE Access2169-35362020-01-018193271933510.1109/ACCESS.2020.29674028962102Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO SystemsXiaolong Bao0https://orcid.org/0000-0001-5839-5146Wenjiang Feng1https://orcid.org/0000-0002-8615-8620Jiali Zheng2https://orcid.org/0000-0002-3472-6630Jingfu Li3https://orcid.org/0000-0002-0079-9129College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaCollege of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaMillimeter wave (mmWave) system tends to have a large number of antenna elements to compensate for the high channel path loss. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, two-step hybrid precoding algorithms that enable the use of fewer RF chains have been proposed. However, the precoding schemes already in place are either too complex or not performing well enough. In this study, an equivalent channel hybrid precoding was proposed. The part from the transmitter RF chain to the receiver RF chain is regarded as equivalent channel. By reducing the dimension of channel matrix to the level of RF link number, baseband pre-coder is simply calculated from decomposing the equivalent channel matrix H<sub>equ</sub>, which greatly reduces the complexity. Based on this novel precoding approach and convolutional neural network (CNN), a novel combiner neural network architecture was also proposed, which can be trained to learn how to optimize the combiner for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed approaches achieve significant performance improvement.https://ieeexplore.ieee.org/document/8962102/mmWaveMIMOhybrid precodingdeep learningCNN |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaolong Bao Wenjiang Feng Jiali Zheng Jingfu Li |
spellingShingle |
Xiaolong Bao Wenjiang Feng Jiali Zheng Jingfu Li Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems IEEE Access mmWave MIMO hybrid precoding deep learning CNN |
author_facet |
Xiaolong Bao Wenjiang Feng Jiali Zheng Jingfu Li |
author_sort |
Xiaolong Bao |
title |
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems |
title_short |
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems |
title_full |
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems |
title_fullStr |
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems |
title_full_unstemmed |
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems |
title_sort |
deep cnn and equivalent channel based hybrid precoding for mmwave massive mimo systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Millimeter wave (mmWave) system tends to have a large number of antenna elements to compensate for the high channel path loss. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, two-step hybrid precoding algorithms that enable the use of fewer RF chains have been proposed. However, the precoding schemes already in place are either too complex or not performing well enough. In this study, an equivalent channel hybrid precoding was proposed. The part from the transmitter RF chain to the receiver RF chain is regarded as equivalent channel. By reducing the dimension of channel matrix to the level of RF link number, baseband pre-coder is simply calculated from decomposing the equivalent channel matrix H<sub>equ</sub>, which greatly reduces the complexity. Based on this novel precoding approach and convolutional neural network (CNN), a novel combiner neural network architecture was also proposed, which can be trained to learn how to optimize the combiner for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed approaches achieve significant performance improvement. |
topic |
mmWave MIMO hybrid precoding deep learning CNN |
url |
https://ieeexplore.ieee.org/document/8962102/ |
work_keys_str_mv |
AT xiaolongbao deepcnnandequivalentchannelbasedhybridprecodingformmwavemassivemimosystems AT wenjiangfeng deepcnnandequivalentchannelbasedhybridprecodingformmwavemassivemimosystems AT jializheng deepcnnandequivalentchannelbasedhybridprecodingformmwavemassivemimosystems AT jingfuli deepcnnandequivalentchannelbasedhybridprecodingformmwavemassivemimosystems |
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