Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments
- Author
- Ruben Mennes, Maxim Claeys, Felipe Augusto Pereira de Figueiredo, Irfan Jabandžić (UGent) , Ingrid Moerman (UGent) and Steven Latré (UGent)
- Organization
- Abstract
- With a growing number of connected devices relying on the Industrial, Scientific, and Medical radio bands for communication, spectrum scarcity is one of the most important challenges currently and in the future. The existing collision avoidance techniques either apply a random back-off when spectrum collision is detected or assume that the knowledge about other nodes' spectrum occupation is known. While these solutions have shown to perform reasonably well in intra-Radio Access Technology environments, they can fail if they are deployed in dense multi-technology environments as they are unable to address the inter-Radio Access Technology interference. In this paper, we present Spectrum Prediction Collision Avoidance (SPCA): an algorithm that can predict the behavior of other surrounding networks, by using supervised deep learning; and adapt its behavior to increase the overall throughput of both its own Multiple Frequencies Time Division Multiple Access network as well as that of the other surrounding networks. We use Convolutional Neural Network (CNN) that predicts the spectrum usage of the other neighboring networks. Through extensive simulations, we show that the SPCA is able to reduce the number of collisions from 50% to 11%, which is 4.5 times lower than the regular Multiple Frequencies Time Division Multiple Access (MF-TDMA) approach. In comparison with an Exponentially Weighted Moving Average (EWMA) scheduler, SPCA reduces the number of collisions from 29% to 11%, which is a factor 2.5 lower.
- Keywords
- Collaborative wireless networks, deep learning, machine learning, wireless MAC
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8616034
- MLA
- Mennes, Ruben, et al. “Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments.” IEEE ACCESS, vol. 7, Ieee-inst Electrical Electronics Engineers Inc, 2019, pp. 45818–30, doi:10.1109/ACCESS.2019.2909398.
- APA
- Mennes, R., Claeys, M., Pereira de Figueiredo, F. A., Jabandžić, I., Moerman, I., & Latré, S. (2019). Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments. IEEE ACCESS, 7, 45818–45830. https://doi.org/10.1109/ACCESS.2019.2909398
- Chicago author-date
- Mennes, Ruben, Maxim Claeys, Felipe Augusto Pereira de Figueiredo, Irfan Jabandžić, Ingrid Moerman, and Steven Latré. 2019. “Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments.” IEEE ACCESS 7: 45818–30. https://doi.org/10.1109/ACCESS.2019.2909398.
- Chicago author-date (all authors)
- Mennes, Ruben, Maxim Claeys, Felipe Augusto Pereira de Figueiredo, Irfan Jabandžić, Ingrid Moerman, and Steven Latré. 2019. “Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments.” IEEE ACCESS 7: 45818–45830. doi:10.1109/ACCESS.2019.2909398.
- Vancouver
- 1.Mennes R, Claeys M, Pereira de Figueiredo FA, Jabandžić I, Moerman I, Latré S. Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments. IEEE ACCESS. 2019;7:45818–30.
- IEEE
- [1]R. Mennes, M. Claeys, F. A. Pereira de Figueiredo, I. Jabandžić, I. Moerman, and S. Latré, “Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments,” IEEE ACCESS, vol. 7, pp. 45818–45830, 2019.
@article{8616034,
abstract = {{With a growing number of connected devices relying on the Industrial, Scientific, and Medical radio bands for communication, spectrum scarcity is one of the most important challenges currently and in the future. The existing collision avoidance techniques either apply a random back-off when spectrum collision is detected or assume that the knowledge about other nodes' spectrum occupation is known. While these solutions have shown to perform reasonably well in intra-Radio Access Technology environments, they can fail if they are deployed in dense multi-technology environments as they are unable to address the inter-Radio Access Technology interference. In this paper, we present Spectrum Prediction Collision Avoidance (SPCA): an algorithm that can predict the behavior of other surrounding networks, by using supervised deep learning; and adapt its behavior to increase the overall throughput of both its own Multiple Frequencies Time Division Multiple Access network as well as that of the other surrounding networks. We use Convolutional Neural Network (CNN) that predicts the spectrum usage of the other neighboring networks. Through extensive simulations, we show that the SPCA is able to reduce the number of collisions from 50% to 11%, which is 4.5 times lower than the regular Multiple Frequencies Time Division Multiple Access (MF-TDMA) approach. In comparison with an Exponentially Weighted Moving Average (EWMA) scheduler, SPCA reduces the number of collisions from 29% to 11%, which is a factor 2.5 lower.}},
author = {{Mennes, Ruben and Claeys, Maxim and Pereira de Figueiredo, Felipe Augusto and Jabandžić, Irfan and Moerman, Ingrid and Latré, Steven}},
issn = {{2169-3536}},
journal = {{IEEE ACCESS}},
keywords = {{Collaborative wireless networks,deep learning,machine learning,wireless MAC}},
language = {{eng}},
pages = {{45818--45830}},
publisher = {{Ieee-inst Electrical Electronics Engineers Inc}},
title = {{Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments}},
url = {{http://doi.org/10.1109/ACCESS.2019.2909398}},
volume = {{7}},
year = {{2019}},
}
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