Performance Study of Model-Driven Based Neural Network for VLC Channel Impairments Compensation

Author:

Miao Pu1,Peng Hui2,Yao Yu3,Chen Peng4,Tian Darming1

Affiliation:

1. School of Electronic and Information Engineering, Qingdao University,Qingdao,China

2. Normal College of Qingdao University,Qingdao,China

3. School of Information Engineering, East China Jiaotong University,Nanchang,China

4. School of Information Sciences and Engineering, Southeast University,Nanjing,China

Funder

Shandong Provincial Natural Science Foundation

China Postdoctoral Science Foundation

China Scholarship Council

Publisher

IEEE

Reference15 articles.

1. Convolutional recurrent neural network-based channel equalization: An experimen-tal study;yang;2017 23rd Asia-Pacific Conference on Communications (APCC) APCC,2018

2. Sparsity-aware channel estimation for mmWave massive MIMO: A deep CNN-based approach

3. Volterra-Aided Neural Network Equalization for Channel Impairment Compensation in Visible Light Communication System

4. Convolution-Enhanced LSTM Neural Network Post-Equalizer used in Probabilistic Shaped Underwater VLC System

5. MTC-Net: Multi-task complex network for concurrent channel estimation and equalization;bahn;2021 IEEE Glob Commun Conf (GLOBECOM),2021

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