A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems
Reliable signal detection plays an essential role in enhancing the quality of signal transmission in wireless communication systems. In this paper, we combine signal detection theory with a deep learning model and propose a novel signal detection scheme based on adaptive ensemble long short term mem...
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doaj-44f9f27456bb4a399bbb02158b9ce21b2021-03-30T02:19:30ZengIEEEIEEE Access2169-35362020-01-01812351412352310.1109/ACCESS.2020.30062659130661A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE SystemsYuanjian Qiao0https://orcid.org/0000-0003-1229-8599Jun Li1https://orcid.org/0000-0001-5099-3475Bo He2https://orcid.org/0000-0002-6418-561XWenxin Li3https://orcid.org/0000-0001-8323-9268Tongliang Xin4https://orcid.org/0000-0002-9463-0323School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaDepartment of Physics, School of Electronic Information Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Information Science and Engineering, Shandong University–Qingdao, Qingdao, ChinaSchool of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaReliable signal detection plays an essential role in enhancing the quality of signal transmission in wireless communication systems. In this paper, we combine signal detection theory with a deep learning model and propose a novel signal detection scheme based on adaptive ensemble long short term memory (AE-LSTM) neural network to handle wireless single carrier frequency domain equalization (SC-FDE) systems in an end-to-end manner. The feature information used for offline training of the deep learning model is extracted from the received signal containing channel state information (CSI) after the multi-path channel and fast Fourier transform (FFT), and the labels are assigned according to the constellation map adopted at the transmitter. To improve the adaptability of the system, we utilize the received power under different delays as the adaptive factor to integrate the output of each sub-network. Then the original data generated by the channel model is recovered by using the trained model instead of channel estimation and frequency domain equalization. Comparative experiments on SC-FDE symbol detection demonstrate that the proposed scheme achieves better performance in terms of reliability than the traditional scheme and the similar deep learning scheme.https://ieeexplore.ieee.org/document/9130661/Deep learningadaptive ensemblesignal detectionSC-FDEchannel estimationfrequency domain equalization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuanjian Qiao Jun Li Bo He Wenxin Li Tongliang Xin |
spellingShingle |
Yuanjian Qiao Jun Li Bo He Wenxin Li Tongliang Xin A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems IEEE Access Deep learning adaptive ensemble signal detection SC-FDE channel estimation frequency domain equalization |
author_facet |
Yuanjian Qiao Jun Li Bo He Wenxin Li Tongliang Xin |
author_sort |
Yuanjian Qiao |
title |
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems |
title_short |
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems |
title_full |
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems |
title_fullStr |
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems |
title_full_unstemmed |
A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems |
title_sort |
novel signal detection scheme based on adaptive ensemble deep learning algorithm in sc-fde systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Reliable signal detection plays an essential role in enhancing the quality of signal transmission in wireless communication systems. In this paper, we combine signal detection theory with a deep learning model and propose a novel signal detection scheme based on adaptive ensemble long short term memory (AE-LSTM) neural network to handle wireless single carrier frequency domain equalization (SC-FDE) systems in an end-to-end manner. The feature information used for offline training of the deep learning model is extracted from the received signal containing channel state information (CSI) after the multi-path channel and fast Fourier transform (FFT), and the labels are assigned according to the constellation map adopted at the transmitter. To improve the adaptability of the system, we utilize the received power under different delays as the adaptive factor to integrate the output of each sub-network. Then the original data generated by the channel model is recovered by using the trained model instead of channel estimation and frequency domain equalization. Comparative experiments on SC-FDE symbol detection demonstrate that the proposed scheme achieves better performance in terms of reliability than the traditional scheme and the similar deep learning scheme. |
topic |
Deep learning adaptive ensemble signal detection SC-FDE channel estimation frequency domain equalization |
url |
https://ieeexplore.ieee.org/document/9130661/ |
work_keys_str_mv |
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