EE Seminar :SSemi-supervised channel equalization using variational autoencoders
סמינר זה יחשב כסמינר שמיעה לתלמידי תואר שני ושלישי
Electrical Engineering Systems Seminar
Speaker: Eli Bery
M.Sc. student under the supervision of Prof. David Burshtein
Monday, 29th July 2024, at 15:30
Room 011, Kitot Building, Faculty of Engineering
Semi-supervised channel equalization using variational autoencoders
Abstract
This research presents methods for semi-supervised learning (SSL) from few pilot signals over nonlinear channels, using variational autoencoders (VAEs). These channels, unknown to the receiver, may have finite memory (intersymbol interference), making traditional supervised learning approaches suboptimal.
Our SSL approach leverages both labeled pilot symbols and unlabeled payload symbols, significantly reducing the number of pilot symbols required for reliable channel inference compared to standard supervised learning methods. The research demonstrates that SSL with VAEs achieves superior performance, yielding a lower error rate and greater efficiency in symbol decoding. For sufficiently many payload symbols, the VAE also has a lower error rate compared to meta-learning that uses the pilot data of the present as well as previous transmission blocks. This advancement in deep learning for communications over unknown nonlinear channels highlights the potential of VAEs in optimizing decoding processes with minimal pilot data.
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