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Master-Vortrag: Autoencoder based Waveform Enhancement for Software Defined Radio Applications
Vitor Horst Duque
Freitag, 2. September 2022
Deep learning is a trend that has made it's way into communication systems and gained a lot of interest already. This presentation discusses a machine learning-based approach to add novel features to existing waveform implementations for software defined radios. Such additional features include the reduction of the peak-to-average power ratio, spread spectrum abilities and interference mitigation. The developed machine learning structures use autoencoders to add these features. The results indicate that realizing additional features by means of autoencoders is a promising concept for future waveform development.