A deep learning wireless transceiver with fully learned modulation and synchronization
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
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.
Details
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Communications Workshops |
Publisher | IEEE |
Number of pages | 6 |
Publication status | Published - 11 Jul 2019 |
Peer-reviewed | Yes |
Externally published | Yes |
Conference
Title | 2019 IEEE International Conference on Communications |
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Subtitle | Empowering Intelligent Communications |
Abbreviated title | ICC 2019 |
Duration | 20 - 24 May 2019 |
Website | |
Degree of recognition | International event |
City | Shanghai |
Country | China |
External IDs
Scopus | 85070271544 |
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