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High-Harmonic and Terahertz Spectroscopy (HATS): Methods and Applications
Open AccessArticle

The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems

by Lili Hao 1,2,*,†, Dongyi Wang 2,†, Yang Tao 2, Wenyong Cheng 3, Jing Li 4 and Zehan Liu 1
1
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
2
Bio-Imaging and Machine Vision Lab, Fischell Department of Bioengineering, University of Maryland, College Park, MA 20740, USA
3
Advanced Research Center for Optics, Shandong University, Jinan 250100, China
4
CETC key laboratory of aerospace information applications, Shijiazhuang 050081, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(5), 852; https://doi.org/10.3390/app9050852
Received: 2 January 2019 / Revised: 5 February 2019 / Accepted: 19 February 2019 / Published: 27 February 2019
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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. View Full-Text
Keywords: orthogonal frequency division multiplexing; autoencoder; end-to-end learning; peak-to-average power ratio orthogonal frequency division multiplexing; autoencoder; end-to-end learning; peak-to-average power ratio
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Hao, L.; Wang, D.; Tao, Y.; Cheng, W.; Li, J.; Liu, Z. The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems. Appl. Sci. 2019, 9, 852.

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