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Block-Structured Deep Learning-Based OFDM Channel Equalization
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DOI：10.1109/LCOMM.2021.3133018.png)
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Abstract
En 中文
This letter considers equalization of an optical signal under the Orthogonal Frequency Division Multiplexing modulation scheme. The equalizer mitigates nonlinear effects caused by a power amplifier (PA) in the transmitter and a transimpedance amplifier (TIA) in the receiver. We compare the Convolutional Neural Network (CNN) and the Wiener-Hammerstein (WH) linearization models using two PA and three TIA devices. We are first to demonstrate a great boost in CNN effectiveness if sequentially combined with a linear equalization. The optimal sequence of linear and nonlinear blocks depends on the device profiles, and is found to be the same between CNN and WH. We develop a new block-structured CNN-based solution that utilizes the optimal sequence and brings up to 2.88 dB Q-factor gain over the traditional WH approach.
Keywords:
Convolutional neural networks
OFDM
Optical transmitters
Decoding
Equalizers
Optical receivers
Optical attenuators
Error compensation
equalizers
optical receivers
multiplexing
neural network applications