The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
End-to-end learning in optical communication systems is a promising technique to solve difficult communication problems, especially for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The less complex, highly adaptive hardware and advantages...
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doaj-a03236082c6040ea9c544a3c83052e002020-11-24T21:35:54ZengMDPI AGApplied Sciences2076-34172019-02-019585210.3390/app9050852app9050852The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM SystemsLili Hao0Dongyi Wang1Yang Tao2Wenyong Cheng3Jing Li4Zehan Liu5School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaBio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MA 20740, USABio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MA 20740, USAAdvanced Research Center for Optics, Shandong University, Jinan 250100, ChinaCETC key laboratory of aerospace information applications, Shijiazhuang 050081, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaEnd-to-end learning in optical communication systems is a promising technique to solve difficult communication problems, especially for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The less complex, highly adaptive hardware and advantages in the analysis of unknown or complex channels make deep learning a valid tool to improve system performance. In this paper, we propose an autoencoder network combined with extended selected mapping methods (ESLM-AE) to reduce the PAPR for the DC-biased optical OFDM system and to minimize the bit error rate (BER). The constellation mapping/de-mapping of the transmitted symbols and the phase factor of each subcarrier are acquired and optimized adaptively by training the autoencoder with a combined loss function. In the loss function, both the PAPR and BER performance are taken into account. The simulation results show that a significant PAPR reduction of more than 10 dB has been achieved by using the ESLM-AE scheme in terms of the complementary cumulative distribution function. Furthermore, the proposed scheme exhibits better BER performance compared to the standard PAPR reduction methods.https://www.mdpi.com/2076-3417/9/5/852orthogonal frequency division multiplexingautoencoderend-to-end learningpeak-to-average power ratio |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lili Hao Dongyi Wang Yang Tao Wenyong Cheng Jing Li Zehan Liu |
spellingShingle |
Lili Hao Dongyi Wang Yang Tao Wenyong Cheng Jing Li Zehan Liu The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems Applied Sciences orthogonal frequency division multiplexing autoencoder end-to-end learning peak-to-average power ratio |
author_facet |
Lili Hao Dongyi Wang Yang Tao Wenyong Cheng Jing Li Zehan Liu |
author_sort |
Lili Hao |
title |
The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems |
title_short |
The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems |
title_full |
The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems |
title_fullStr |
The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems |
title_full_unstemmed |
The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems |
title_sort |
extended slm combined autoencoder of the papr reduction scheme in dco-ofdm systems |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-02-01 |
description |
End-to-end learning in optical communication systems is a promising technique to solve difficult communication problems, especially for peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The less complex, highly adaptive hardware and advantages in the analysis of unknown or complex channels make deep learning a valid tool to improve system performance. In this paper, we propose an autoencoder network combined with extended selected mapping methods (ESLM-AE) to reduce the PAPR for the DC-biased optical OFDM system and to minimize the bit error rate (BER). The constellation mapping/de-mapping of the transmitted symbols and the phase factor of each subcarrier are acquired and optimized adaptively by training the autoencoder with a combined loss function. In the loss function, both the PAPR and BER performance are taken into account. The simulation results show that a significant PAPR reduction of more than 10 dB has been achieved by using the ESLM-AE scheme in terms of the complementary cumulative distribution function. Furthermore, the proposed scheme exhibits better BER performance compared to the standard PAPR reduction methods. |
topic |
orthogonal frequency division multiplexing autoencoder end-to-end learning peak-to-average power ratio |
url |
https://www.mdpi.com/2076-3417/9/5/852 |
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