研究目的
Investigating the effectiveness of a post equalization scheme based on Deep Neural Network (DNN) for DFT-S OFDM modulation using Probabilistic Shaping (PS) technique in underwater visible light communication (VLC) system.
研究成果
The DNN-based post equalization scheme significantly improves the system capacity and reduces BER for odd order QAM PS technique in underwater VLC systems, demonstrating its potential for future high-speed underwater optical communications.
研究不足
The study is limited to a 1.2meter underwater optical transmission and specific modulation schemes (PS128QAM DFT-S OFDM). The DNN's effectiveness is evaluated under certain conditions (bias current and peak-to-peak voltage), which may not cover all operational scenarios.
1：Experimental Design and Method Selection:
The study employs a DNN-based post equalization scheme for DFT-S OFDM modulation using PS technique in underwater VLC system.
2：Sample Selection and Data Sources:
The experiment involves transmitting PS128QAM DFT-S OFDM modulated signals over a
3：2meter underwater optical channel. List of Experimental Equipment and Materials:
Includes an arbitrary waveform generator (AWG, Tektronix), blue LED as transmitter, photodiode with differential output, and a real-time oscilloscope with 5 GSa/s sampling rate.
4：Experimental Procedures and Operational Workflow:
The process involves probabilistic shaping with M-B distribution, DFT, IDFT, pulse shaping by AWG, transmission over underwater channel, photoelectric conversion, digitization, DNN processing, and BER measurement.
5：Data Analysis Methods:
The performance is evaluated based on BER measurements and system capacity improvement.
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