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Deep Learning for Over-the-Air Non-Orthogonal Signal Classification

Xu, T; Darwazeh, I; (2020) Deep Learning for Over-the-Air Non-Orthogonal Signal Classification. In: Proceedings of IEEE 91st Vehicular Technology Conference: VTC2020-Spring. IEEE: Antwerp, Belgium. Green open access

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Abstract

Non-cooperative communications, where a receiver can automatically distinguish and classify transmitted signal formats prior to detection, are desirable for low-cost and low-latency systems. This work focuses on the deep learning enabled blind classification of multi-carrier signals covering their orthogonal and non-orthogonal varieties. We define Type-I signals with large feature diversity and Type-II signals with strong feature similarity. We evaluate time-domain and frequency-domain convolutional neural network (CNN) models with wireless channel/hardware impairments. Experimental systems are designed and tested, using software defined radio (SDR) devices, operated for different signal formats in line-of-sight and non-line-of-sight communication link scenarios. Testing, using four different time-domain CNN models, showed the pre-trained CNN models to have limited efficiency and utility due to the mismatch between the analytical/simulation and practical/real-world environments. Transfer learning, which is an approach to fine-tune learnt signal features, is applied based on measured over-the-air time-domain signal samples. Experimental results indicate that transfer learning based CNN can efficiently distinguish different signal formats for Type-I in both line-of-sight and non-line-of-sight scenarios relative to the non-transfer-learning approaches. Type-II signals are not identified correctly in the experiment even with the transfer learning assistance leading to potential applications in secure communications.

Type: Proceedings paper
Title: Deep Learning for Over-the-Air Non-Orthogonal Signal Classification
Event: IEEE 91st Vehicular Technology Conference: VTC2020-Spring
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/VTC2020-Spring48590.2020.9128869
Publisher version: https://doi.org/10.1109/VTC2020-Spring48590.2020.9...
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10093299
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