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

Abstract

This paper looks into the technology 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, or online classification on the low cost sensors is studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

References in corpus (1)

• Electrosense: Open and Big Spectrum Data

Cited by in corpus (17)

• Electrosense: Open and Big Spectrum Data
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• Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing
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• Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification
• Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
• Automatic Modulation Classification Using Involution Enabled Residual Networks
• Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN
• Robust Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulant Features
• Using Capsule Networks to Classify Digitally Modulated Signals with Raw I/Q Data
• On Deep Learning Classification of Digitally Modulated Signals Using Raw I/Q Data
• Self-supervised Learning for Clustering of Wireless Spectrum Activity
• Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation
• Accurate Unsupervised Photon Counting from Transition Edge Sensor Signals
• Deep Feature Learning for Wireless Spectrum Data
• Spectrum Sensing with Deep Clustering: Label-Free Radio Access Technology Recognition