Deep Neural Networks for Multicarrier Waveforms Classification in UAV Networks

被引：1

作者：

Zerhouni, Kawtar ^{[1
]}

Amhoud, El Mehdi ^{[1
]}

Noubir, Guevara ^{[2
]}

机构：

[1] Mohammed VI Polytech Univ, Sch Comp Sci, Benguerir, Morocco

[2] Northeastern Univ, Khoury Coll Comp Sci, Boston, MA 02115 USA

来源：

2021 17TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATION SYSTEMS, ISWCS | 2021年

关键词：

Unmanned aerial vehicle; automatic signal recognition; multicarrier waveform; deep learning; convolutional neural networks;

D O I：

10.1109/ISWCS49558.2021.9562225

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

During the last decade, the adoption of Unmanned aerial vehicles (UAV) for wireless networks has been rapidly growing for both civilian and military applications, due to their flexibility and mobility. In this paper, we consider their use as an aerial base station, then investigate the task of automatic signal recognition (ASR) through a deep learning (DL) approach, while considering spectrally efficient filtered multicarrier waveforms, including UFMC, F-OFDM, FBMC and GFDM compared to the legacy OFDM. This study has been done through series of simulations; in each one, different filter sizes, number of filters and number of convolutional layers have been applied. The best performance, with an overall accuracy of 0.93 at 2 dB signal to noise ratio (SNR), has been obtained when using smaller networks, with smaller filter sizes and higher number of filters.

引用

页数：6

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