Transcription of An Introduction to Deep Learning for the Physical Layer
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An Introduction to Deep Learning for the Physical Layer
1An Introduction to deep Learningfor the Physical LayerTim O Shea,Senior Member, IEEE,and Jakob Hoydis,Member, IEEEA bstract We present and discuss several novel applicationsof deep Learning (DL) for the Physical Layer . By interpretinga communications system as an autoencoder, we develop afundamental new way to think about communications systemdesign as an end-to-end reconstruction task that seeks to jointlyoptimize transmitter and receiver components in a single show how this idea can be extended to networks of multipletransmitters and receivers and present the concept of radiotransformer networks (RTNs) as a means to incorporate expertdomain knowledge in the machine Learning (ML) model. Lastly,we demonstrate the application of convolutional neural networks(CNNs) on raw IQ samples for modulation classification whichachieves competitive accuracy with respect to traditional schemesrelying on expert features.
1 An Introduction to Deep Learning for the Physical Layer Tim O’Shea, Senior Member, IEEE, and Jakob Hoydis, Member, IEEE Abstract—We present and discuss several novel applications
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