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14 February 2019 Frequency reshaping and compensation scheme based on deep neural network for a FTN CAP 9QAM signal in visible light communication system
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Proceedings Volume 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018); 110482F (2019) https://doi.org/10.1117/12.2523089
Event: 17th International Conference on Optical Communications and Networks (ICOCN2018), 2018, Zhuhai, China
Abstract
We have proposed a frequency reshaping and compensation scheme for FTN (Faster-Than-Nyquist) CAP (Carrierless Amplitude and Phase modulation) signal based on machine learning for the first time in this paper. The cascaded post equalizer consists of 2 stages. The first stage digital signal processing (DSP) is a Deep Neural Network (DNN), then LMS post-equalization is conducted as the second stage offline DSP. Through this method, we successfully demonstrated a data rate of 1.12Gbit/s FTN CAP 9QAM modulation over 1meter free space transmission with bit error rate (BER) below 7% FEC threshold of 3.8×10-3. Compared to the traditional FTN CAP modulation without DNN, the proposed method would increase the data rate by approximately 100 Mbps, leading to an improvement of system capacity of 9.8%. The experimental results clearly validate the proposed cascaded two stage DNN-LMS (C2S DNNLMS) can be a promising solution for future high speed VLC system.
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Yinaer Ha, Wenqing Niu, and Nan Chi "Frequency reshaping and compensation scheme based on deep neural network for a FTN CAP 9QAM signal in visible light communication system", Proc. SPIE 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018), 110482F (14 February 2019); https://doi.org/10.1117/12.2523089
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