Deep Learning Based Communication Over the Air

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiab...

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Vydané v:IEEE journal of selected topics in signal processing Ročník 12; číslo 1; s. 132 - 143
Hlavní autori: Dorner, Sebastian, Cammerer, Sebastian, Hoydis, Jakob, Brink, Stephan ten
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.02.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Autoencoder
communication
Communication systems
Communications systems
Computer simulation
Data transmission
Deep learning
end-to-end learning
Hardware
modulation
neural network
Neural networks
over-the-air
Receivers
software-defined radio
Source code
Synchronism
Synchronization
Training
Transmitters
ISSN:1932-4553, 1941-0484
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Shrnutí:End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios and open-source deep learning software libraries. We extend the existing ideas toward continuous data transmission, which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the "learned" system with that of a practical baseline shows competitive performance close to 1 dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular, the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.
Bibliografia:ObjectType-Article-1
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2017.2784180