IMDEA Networks Institute Publications Repository

Deep Learning Models for Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors

Rajendran, Sreeraj and Meert, Wannes and Giustiniano, Domenico and Lenders, Vincent and Pollin, Sofie (2018) Deep Learning Models for Wireless Signal Classification with Distributed Low-Cost Spectrum Sensors. [Journal Articles]

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Abstract

This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data and on-line classification on the low-cost spectrum sensors are studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

Item Type: Journal Articles
Uncontrolled Keywords: Communication system security; Data models; Machine learning; Modulation; Sensors; Wireless communication; Wireless sensor networks; CNN; Deep learning; LSTM; Modulation classification; Spectrum sensing.
Subjects: UNSPECIFIED
Divisions: UNSPECIFIED
Depositing User: Domenico Giustiniano
Date Deposited: 16 Jul 2018 14:47
Last Modified: 30 Jul 2018 13:16
URI: http://eprints.networks.imdea.org/id/eprint/1847

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