A Channel Adaptive MIMO Spatial Multiplexing UWOC System Based on Deep Learning Encoder/Decoder

The absorption, scattering and turbulence have a significant and time-varying impact on light transmission underwater. The light-emitting diodes (LED) as a light source in an underwater wireless optical communication (UWOC) system significantly minimize the requirement for the transceiver alignment,...

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Veröffentlicht in:2023 4th Information Communication Technologies Conference (ICTC) S. 27 - 33
Hauptverfasser: Zeng, Lingbiao, Yin, Hongxi, Wang, Xinge, Ji, Xiuyang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.05.2023
Schlagworte:
Absorption
Adaptive systems
channel adaptive
Deep learning
Light emitting diodes
MIMO
Optical fiber communication
Scattering
spatial multiplexing
underwater wireless optical communication
Wireless communication
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Abstract The absorption, scattering and turbulence have a significant and time-varying impact on light transmission underwater. The light-emitting diodes (LED) as a light source in an underwater wireless optical communication (UWOC) system significantly minimize the requirement for the transceiver alignment, but the capacity and the reliability of the system are constrained by the narrow bandwidth of the LED. To this end, this paper proposes a channel adaptive multiple-input multiple-output (MIMO) spatial multiplexing (SM) system based on the deep learning encoder/decoder to adapt to the channel variation and optimize system performance by learning different water channels and different modulation and demodulation methods. Our simulation experiment demonstrates that EDCC has lower SER performance and better robustness in turbulent channels than conventional LS-MMSE, LS-ML, and deep learning based DNN-D and DNN-ED algorithms.
AbstractList The absorption, scattering and turbulence have a significant and time-varying impact on light transmission underwater. The light-emitting diodes (LED) as a light source in an underwater wireless optical communication (UWOC) system significantly minimize the requirement for the transceiver alignment, but the capacity and the reliability of the system are constrained by the narrow bandwidth of the LED. To this end, this paper proposes a channel adaptive multiple-input multiple-output (MIMO) spatial multiplexing (SM) system based on the deep learning encoder/decoder to adapt to the channel variation and optimize system performance by learning different water channels and different modulation and demodulation methods. Our simulation experiment demonstrates that EDCC has lower SER performance and better robustness in turbulent channels than conventional LS-MMSE, LS-ML, and deep learning based DNN-D and DNN-ED algorithms.
Author Wang, Xinge
Yin, Hongxi
Zeng, Lingbiao
Ji, Xiuyang
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  givenname: Xiuyang
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  email: bluelightningknight@foxmail.com
  organization: Dalian University of Technology,School of Information and Communication Engineering,Dalian,China
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Snippet The absorption, scattering and turbulence have a significant and time-varying impact on light transmission underwater. The light-emitting diodes (LED) as a...
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StartPage 27
SubjectTerms Absorption
Adaptive systems
channel adaptive
Deep learning
Light emitting diodes
MIMO
Optical fiber communication
Scattering
spatial multiplexing
underwater wireless optical communication
Wireless communication
Title A Channel Adaptive MIMO Spatial Multiplexing UWOC System Based on Deep Learning Encoder/Decoder
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