Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158355
Title: Deep learning in channel estimation and signal detection in OFDM systems
Authors: Wang, Zefan
Keywords: Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wang, Z. (2022). Deep learning in channel estimation and signal detection in OFDM systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158355
Abstract: This dissertation presents the results of channel estimation and signal detection using deep learning in Orthogonal Frequency Division Multiplexing (OFDM) system. In this dissertation, deep learning is used to deal with wireless OFDM channel. In the existing method, the channel state information is estimated first, and then the estimated channel state information is used to detect / recover the OFDM receiver of the transmission symbol. The method based on deep learning proposed in this dissertation implicitly estimates the channel state information and directly recovers the transmission symbols. In order to solve the channel distortion, the deep learning model first uses the data generated by the simu- lation based on channel statistics for offline training, and then directly restores the data transmitted online. From the simulation results, the method based on deep learning is more robust than the traditional method. In conclusion, deep learning is a useful method in signal detection and channel estimation in complex channel with distortion.
URI: https://hdl.handle.net/10356/158355
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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