Optimization of Optical Imaging MIMO-OFDM Precoding Matrix for Underwater VLC

  • Yanlong Li
  • Hongbing Qiu
  • Xiao Chen
  • Jielin FuEmail author
  • Junyi Wang
  • Yitao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)


The absorption and scattering are the main problems affecting high-speed data transmission in underwater visible light communication system (UVLC). To address these problems, we propose the imaging multiple input multiple output (MIMO) system for the underwater communication in this paper. Furthermore, the proposed system uses imaging lens to separate the light signal resulting in that decreasing disturbance of the proposed system is better than that of non-imaging MIMO. In this paper, aiming at the problem of high bit error rate (BER) caused by channel correlation in underwater imaging optical MIMO communication system, a precoding algorithm based on received signal Euclidean distance of imaging multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is proposed. In order to maximize the minimum Euclidean distance of the received signal set, the precoding matrix is solved under the constraints of the non-negative optical signal and the total power. The system uses the precoding matrix to precode the signals and the receiver detects signals through the maximum likelihood algorithm with the channel matrix and the optimal precoding matrix. The simulation results show that the imaging MIMO system achieves 12 dB gain at the same bit error rate (BER) compared to non-imaging MIMO. Furthermore, the proposed algorithm based on received signal Euclidean distance achieves about 5 dB gain under the same channel compared to the SVD-based precoding algorithm in imaging MIMO system, it greatly improve the BER performance of the imaging optical MIMO-OFDM system in UVLC.


Underwater LED communication Imaging MIMO Spatial correlation Euclidean distance Precoding matrix 



This work is supported by the National Natural Science Foundation of China under grant (61761014), Guangxi Natural Science Foundation (2018GXNSFBA281131), Ministry of Education Key Laboratory of Cognitive Radio and Information Processing (CRKL170106), Guangxi key research and development plan(Guike AB18126030), Innovation Project of Guangxi Graduate Education (YCBZ 2017050), Guangxi University Young and Middle-aged Teachers Basic Ability Improvement Plan (2018KY0208), Guangxi Cooperative Innovation Center of cloud computing and Big Data (No1716) and Guangxi Colleges and Universities Key Laboratory of cloud computing and complex systems.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanlong Li
    • 1
    • 2
  • Hongbing Qiu
    • 1
    • 2
  • Xiao Chen
    • 1
  • Jielin Fu
    • 1
    • 2
    Email author
  • Junyi Wang
    • 1
  • Yitao Zhang
    • 1
  1. 1.Department of Information and CommunicationGuilin University of Electronic TechnologyGuilinChina
  2. 2.Ministry of Education Key Laboratory of Cognitive Radio and Information ProcessingGuilin University of Electronic TechnologyGuilinChina

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