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ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
This paper describes the architecture and performance of ORACLE, an appr...
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Machine Learning Approach to RF Transmitter Identification
With the development and widespread use of wireless devices in recent ye...
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Spectrum Data Poisoning with Adversarial Deep Learning
Machine learning has been widely applied in wireless communications. How...
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An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
The exponential growth in the number of complex datasets every year requ...
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Deep Learning for Over-the-Air Non-Orthogonal Signal Classification
Non-cooperative communications, where a receiver can automatically disti...
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Modern CNNs for IoT Based Farms
Recent introduction of ICT in agriculture has brought a number of change...
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Grant-Free Access: Machine Learning for Detection of Short Packets
In this paper, we explore the use of machine learning methods as an effi...
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Transmitter Classification With Supervised Deep Learning
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real world situations where topologies evolve over time. To remedy this, the work rests on a series of datasets gathered in the Future Internet of Things / Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural network (CNN), where focus has been given to reduce channel bias that has plagued previous works and constrained them to a constant environment or to simulations. The most challenging scenarios provide the trained neural network with resilience and show insight on the best signal type to use for identification , namely packet preamble. The generated datasets are published on the Machine Learning For Communications Emerging Technologies Initiatives web site 4 in the hope that they serve as stepping stones for future progress in the area. The community is also invited to reproduce the studied scenarios and results by generating new datasets in FIT/CorteXlab.
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