A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization

Authors

J. Schmitz, C. v. Lengerke, N. Airee, A. Behboodi, R. Mathar,

Abstract

In this paper, we present a deep learning based wireless transceiver. We describe in detail the corresponding artificial neural network architecture, the training process, and report on excessive over-the-air measurement results. We employ the end-to-end training approach with an autoencoder model that includes a channel model in the middle layers as previously proposed in the literature. In contrast to other state-of-the-art results, our architecture supports learning time synchronization without any manually designed signal processing operations. Moreover, the neural transceiver has been tested over the air with an implementation in software defined radio. Our experimental results for the implemented single antenna system demonstrate a raw bit-rate of 0.5 million bits per second. This exceeds results from comparable systems presented in the literature and suggests the feasibility of high throughput deep learning transceivers.

Index Terms

Deep Learning, Transceiver, Wireless Communication, Synchronization

2nd Workshop on Machine Learning in Wireless Communications (ML4COM)

BibT_EX Reference Entry

@inproceedings{ScLeAiBeMa19,
	author = {Johannes Schmitz and Caspar von Lengerke and Nikita Airee and Arash Behboodi and Rudolf Mathar},
	title = "A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization",
	pages = "1-6",
	booktitle = "International Conference on Communications ({ICC})",
	address = {Shanghai, China},
	month = May,
	year = 2019,
	hsb = RWTH-2019-05505,
	}

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