An Introduction to Deep Learning for the Physical Layer

Abstract

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation.

13 pages, 12 figures, 5 tables, under submission to academic journal

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Cited by in corpus (8)

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• Learning to Detect
• Deep Learning Based MIMO Communications
• RNN Decoding of Linear Block Codes
• Optimal Physical Preprocessing for Example-Based Super-Resolution
• AI Coding: Learning to Construct Error Correction Codes
• MIMO Channel Information Feedback Using Deep Recurrent Network
• Bit-level Optimized Neural Network for Multi-antenna Channel Quantization