Data-Driven Channel Modeling for End-to-End Visible Light DCO-OFDM Communication System Based on Experimental Data

被引:1
作者
Song, Bo [1 ]
Zhu, Yanwen [2 ]
Huang, Yi [2 ]
Zong, Haiteng [3 ]
机构
[1] Beijing Smartchip Semicond Technol Co Ltd, Beijing 102299, Peoples R China
[2] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
[3] Beijing Guoyuan Liannuo Technol Co Ltd, Beijing 102299, Peoples R China
关键词
end to end; DCO-OFDM; visible light communication; channel model; deep learning;
D O I
10.3390/photonics11080781
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
End-to-end systems have been introduced to address the issue of independent signal processing module design in traditional communication systems, which prevents achieving global system optimization. However, research on indoor end-to-end Visible Light Communication (VLC) systems remains limited, especially regarding the channel modeling of high-speed, high-capacity Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) systems. This paper proposes three channel modeling methods for end-to-end DCO-OFDM VLC systems. The accuracy of the proposed methods is demonstrated through R-Square model fitting performance and data distribution analysis. The effectiveness of the proposed channel modeling methods is further validated by comparing the bit error rate (BER) performance of traditional receivers and existing deep learning (DL)-based receivers. The results show that the proposed methods can effectively mitigate both linear and nonlinear distortions. By employing these channel modeling methods, communication systems can reduce the spectral occupancy of pilot signals, thereby significantly lowering the complexity of traditional channel estimation methods. Thus, these methods are suitable for use in end-to-end VLC communication systems.
引用
收藏
页数:12
相关论文
共 18 条
[1]   Optical Fiber Channel Modeling Method Using Multi-BiLSTM for PM-QPSK Systems [J].
Cui, Qichuan ;
Wang, Danshi ;
Li, Mingliang ;
Song, Yuchen ;
Li, Jin ;
Zhang, Min .
2020 CONFERENCE ON LASERS AND ELECTRO-OPTICS PACIFIC RIM (CLEO-PR), 2020,
[2]   Investigation of a hybrid OFDM-PWM/PPM visible light communications system [J].
Ebrahimi, Farzaneh ;
Ghassemlooy, Zabih ;
Olyaee, Saeed .
OPTICS COMMUNICATIONS, 2018, 429 :65-71
[3]  
Elgala H., 2009, P 2009 IFIP INT C WI, P1
[4]  
Farsad N, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P2326, DOI 10.1109/ICASSP.2018.8461983
[5]   ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers [J].
Gao, Xuanxuan ;
Jin, Shi ;
Wen, Chao-Kai ;
Li, Geoffrey Ye .
IEEE COMMUNICATIONS LETTERS, 2018, 22 (12) :2627-2630
[6]   Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems [J].
He, Hengtao ;
Wen, Chao-Kai ;
Jin, Shi ;
Li, Geoffrey Ye .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2018, 7 (05) :852-855
[7]   BiGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication [J].
Huang, Yi ;
Han, Dahai ;
Zhang, Min ;
Zhu, Yanwen ;
Wang, Liqiang .
PHOTONICS, 2023, 10 (09)
[8]  
Ji YF, 2019, CHINA COMMUN, V16, P19, DOI 10.12676/j.cc.2019.05.002
[9]   End-to-End Deep Learning of Optical Fiber Communications [J].
Karanov, Boris ;
Chagnon, Mathieu ;
Thouin, Felix ;
Eriksson, Tobias A. ;
Buelow, Henning ;
Lavery, Domanic ;
Bayvel, Polina ;
Schmalen, Laurent .
JOURNAL OF LIGHTWAVE TECHNOLOGY, 2018, 36 (20) :4843-4855
[10]   Deep learning based end-to-end visible light communication with an in-band channel modeling strategy [J].
Li, Zhongya ;
Shi, Jianyang ;
Zhao, Yiheng ;
Li, Guoqiang ;
Chen, Jiang ;
Zhang, Junwen ;
Chi, Nan .
OPTICS EXPRESS, 2022, 30 (16) :28905-28921