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Title: Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system
Authors: Lu, XY 
Lu, C 
Yu, WX
Qiao, L
Liang, SY
Lau, APT 
Chi, N
Issue Date: 2019
Publisher: Optical Society of America
Source: Optics express, 4 Mar. 2019, v. 27, no. 5, p. 7822-7833 How to cite?
Journal: Optics express 
Abstract: 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.
EISSN: 1094-4087
DOI: 10.1364/OE.27.007822
Rights: © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (
The following publication Xingyu Lu, Chao Lu, Weixiang Yu, Liang Qiao, Shangyu Liang, Alan Pak Tao Lau, and Nan Chi, "Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system," Opt. Express 27, 7822-7833 (2019) is available at
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