Name
Abstract | ||
|---|---|---|
The ability to answer all important questions about the radio-frequency (RF) scene is essential for cognitive radios (CRs) to be effective. In this paper, we propose a RF-based automatic traffic recognizer that, observing the radio spectrum emitted by a communication link and exploiting machine learning (ML) techniques, is able to distinguish between two types of data streams. Numerical results based on real waveforms collected by a RF sensor, demonstrate that over-the-air user traffic classification is possible with an accuracy of 97% at high signal-to-noise ratios (SNRs). Moreover, we show that using a neural network (NN) very good classification performance can be achieved also at low SNRs (around 2 dB). Finally, the impact of the observed RF bandwidth and the acquisition time window on the classification accuracy are analyzed in detail. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553196 | European Signal Processing Conference |
Field | DocType | ISSN |
Traffic classification,Wireless network,Computer science,Support vector machine,Feature extraction,Radio frequency,Bandwidth (signal processing),Artificial intelligence,Artificial neural network,Machine learning,Cognitive radio | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Testi | 1 | 0 | 0.34 |
| Elia Favarelli | 2 | 0 | 0.34 |
| Andrea Giorgetti | 3 | 110 | 10.93 |