Xu, T;
Darwazeh, I;
(2019)
Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals.
In:
Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) - 2019.
IEEE: Marrakech, Morocco.
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Abstract
The non-orthogonal IoT signal, following the bandwidth compression spectrally efficient frequency division multiplexing (SEFDM) characteristics, can bring benefits in enhanced massive device connections, signal coverage extension and data rate increase, but at the cost of computational complexity. Resource-constrained IoT devices have limited memory storage and complex signal processing is not allowed. Machine learning can simplify signal detection by training a general data-driven signal detection model. However, fully connected neural networks would introduce processing latency and extra power consumption. Therefore, the motivation of this work is to investigate different neural network compression schemes for system simplification. Three compression strategies are studied including topology compression, weight compression and quantization compression. These methods show efficient neural network compression with trade-offs between computational complexity and bit error rate (BER) performance. Practical neural network prototyping is evaluated as well on a software defined radio (SDR) platform. Results show that the practical weight compression neural network can achieve similar performance as the fully connected neural network but with great resource saving.
| Type: | Proceedings paper |
|---|---|
| Title: | Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals |
| Event: | 2019 IEEE Wireless Communications and Networking Conference (WCNC) |
| Location: | Marrakech, Morocco |
| Dates: | 15 April 2019 - 19 April 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WCNC.2019.8885830 |
| Publisher version: | https://doi.org/10.1109/WCNC.2019.8885830 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Neural network, machine learning, neural network compression, Internet of things, non-orthogonal, spectral efficiency, software defined radio, prototyping. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10067511 |
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