The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). 

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[1] Zhichen Zhang, "SCBiGNet", IEEE Dataport, 2023. [Online]. Available: Accessed: Mar. 29, 2023.
doi = {10.21227/3rj4-s615},
url = {},
author = {Zhichen Zhang },
publisher = {IEEE Dataport},
title = {SCBiGNet},
year = {2023} }
T1 - SCBiGNet
AU - Zhichen Zhang
PY - 2023
PB - IEEE Dataport
UR - 10.21227/3rj4-s615
ER -
Zhichen Zhang. (2023). SCBiGNet. IEEE Dataport.
Zhichen Zhang, 2023. SCBiGNet. Available at:
Zhichen Zhang. (2023). "SCBiGNet." Web.
1. Zhichen Zhang. SCBiGNet [Internet]. IEEE Dataport; 2023. Available from :
Zhichen Zhang. "SCBiGNet." doi: 10.21227/3rj4-s615