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

By Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano, Vincent Lenders, Sofie Pollin. IEEE Transactions on Cognitive Communications and Networking, 2017

780 citations recorded by the source corpus.

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 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 signal-to-noise ratio conditions ranging from 0 dB to 20 dB. 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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/67fb3b536092a10fd01d667734e859600b6d6fd2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Paul Patras
2. Fan Meng
3. Ingrid Moerman
4. Hamed Haddadi
5. Tarik Kazaz
6. Xianbin Wang
7. Peng Chen
8. Merima Kulin

In Computer Networks and Communications

1. Paul Patras
2. Fan Meng
3. Ingrid Moerman
4. Hamed Haddadi
5. Xianbin Wang
6. Peng Chen
7. E. De Poorter
8. Lenan Wu

In Electrical and Electronic Engineering

1. Paul Patras
2. Ingrid Moerman
3. Hamed Haddadi
4. Xianbin Wang
5. E. De Poorter
6. Lenan Wu
7. Chaoyun Zhang
8. Erik G. Larsson

Citations in the corpus

• Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, methodology): “in [30], a LSTM-based AMC is proved to outperform the CNN model with oversampled received signals at small or medium scales.”
• End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, methodology): “) have been classified with high accuracy in [13] using deep learning on averaged magnitude FFT data.”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “Due to its superior performance, an LSTM is also employed for a similar task in [357].”