期刊
OPTICS EXPRESS
卷 27, 期 5, 页码 7822-7833出版社
Optica Publishing Group
DOI: 10.1364/OE.27.007822
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资金
- Hong Kong Polytechnic University [G-SB0U]
- Hong Kong SAR Government General Research Fund (GRF) [PolyU 152109/14E]
- National Natural Science Foundation of China [61571133]
- National Key Research and Development Program of China [2017YFB0403603]
Linear and nonlinear impairments severely limit the transmission performance of high-speed visible light communication systems. Neural network-based equalizers have been applied to optical communication systems, which enables significantly improved system performance, such as transmission data rate and distance. In this paper, a memory-controlled deep long short-term memory (LSTM) neural network post-equalizer is proposed to mitigate both linear and nonlinear impairments in pulse amplitude modulation (PAM) based visible light communication (VLC) systems. Both 1.15-Gbps PAM4 and 0.9Gbps PAM8 VLC systems are successfully demonstrated, based on a single red-LED with bit error ratio (BER) below the hard decision forward error correction (HD-FEC) limit of 3.8 x 10(-3). Compared with the traditional finite impulse response (FIR) based equalizer, the Q factor performance is improved by 1.2dB and the transmission distance is increased by one-third in the same experimental hardware setups. Compared with traditional nonlinear hybrid Volterra equalizers, the significant complexity and system performance advantages of using a LSTM-based equalizer is demonstrated. To the best of our knowledge, this is the first demonstration of using deep LSTM in VLC systems. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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