16QAM-OFDM VLC System Based on Frequency Domain Precompensation and DNN Post-Equalization

Visible light communication (VLC) technology utilizes LEDs as light sources, offering several benefits, such as the use of license-free spectrum, green, and safe. However, conventional LED devices face challenges due to their restricted bandwidth and nonlinearity under different current levels. These issues lead to signal distortion and intersymbol interference (ISI), which are particularly problematic in high-order modulation schemes. In this work, we propose a low-complexity scheme combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation and shallow hidden layer deep neural networks (DNNs)-based post-equalization in a VLC system. At the transmitter, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation is used to suppress the influence of high-frequency power fading. The post-equalization at the receiver side uses the least square (LS)-DNN cascaded equalization algorithm, where LS performs linear equalization and DNN performs nonlinear equalization. Since the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation and LS algorithm have completed partial equalization, it can provide a good initial state for DNN training, thus speeding up the convergence of DNN. To better capture the dependency between symbols, we preprocess the dislocation filling before the data is sent to the input layer of DNN. In the experiment, a VLC system with the 3-dB bandwidth of 9 MHz is used. With <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation, its 3-dB bandwidth is expanded to about 43 MHz. The two hidden layer DNN is used to carry nonlinear equalization. If an 80-Mb/s 16QAM OFDM signal is transmitted in a 3-m VLC system, the scheme proposed can expand the available peak-to-peak voltage <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(V_{\mathrm { pp}})$ </tex-math></inline-formula> range by 30% compared with precompensation LS algorithm and achieve transmission over 9 m under 7% hard-decision forward error correction (HD-FEC) BER threshold of 3.8e-3. The comparison with LSTM-DNN-based scheme shows that the proposed scheme can improve the performance of VLC system in terms of BER and EVM, while the training complexity is 2-order of magnitude lower.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-12-18
DOI
https://doi.org/10.1109/jiot.2024.3519590
Citations
4
Primary Topic
Optical Network Technologies
Type
article
Field-Weighted Citation Impact
0.83

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article

16QAM-OFDM VLC System Based on Frequency Domain Precompensation and DNN Post-Equalization

Aiying Yang, Zhenrong Zhang, Lihui Feng, Shaoxi Ji et al.
4 citations
IEEE Internet of Things Journal
Optical Network Technologies
0.83
article

16QAM-OFDM VLC System Based on Frequency Domain Precompensation and DNN Post-Equalization

Aiying Yang, Zhenrong Zhang, Lihui Feng, Shaoxi Ji, Hang Xu, Minglun Zhang, Hongyang Zhang
article en
4 citations

Abstract

Visible light communication (VLC) technology utilizes LEDs as light sources, offering several benefits, such as the use of license-free spectrum, green, and safe. However, conventional LED devices face challenges due to their restricted bandwidth and nonlinearity under different current levels. These issues lead to signal distortion and intersymbol interference (ISI), which are particularly problematic in high-order modulation schemes. In this work, we propose a low-complexity scheme combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation and shallow hidden layer deep neural networks (DNNs)-based post-equalization in a VLC system. At the transmitter, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation is used to suppress the influence of high-frequency power fading. The post-equalization at the receiver side uses the least square (LS)-DNN cascaded equalization algorithm, where LS performs linear equalization and DNN performs nonlinear equalization. Since the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation and LS algorithm have completed partial equalization, it can provide a good initial state for DNN training, thus speeding up the convergence of DNN. To better capture the dependency between symbols, we preprocess the dislocation filling before the data is sent to the input layer of DNN. In the experiment, a VLC system with the 3-dB bandwidth of 9 MHz is used. With <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> precompensation, its 3-dB bandwidth is expanded to about 43 MHz. The two hidden layer DNN is used to carry nonlinear equalization. If an 80-Mb/s 16QAM OFDM signal is transmitted in a 3-m VLC system, the scheme proposed can expand the available peak-to-peak voltage <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(V_{\mathrm { pp}})$ </tex-math></inline-formula> range by 30% compared with precompensation LS algorithm and achieve transmission over 9 m under 7% hard-decision forward error correction (HD-FEC) BER threshold of 3.8e-3. The comparison with LSTM-DNN-based scheme shows that the proposed scheme can improve the performance of VLC system in terms of BER and EVM, while the training complexity is 2-order of magnitude lower.

IEEE Internet of Things JournalVol. 12(9)
Beijing Institute of Technology (CN), Beijing University of Posts and Telecommunications (CN), Guangxi University (CN)
National Natural Science Foundation of China, Science and Technology Major Project of Guangxi
Openalex Percentile: Top 26%
Optical Network Technologies
0.83
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