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Identification of Communication Signals Using Learning Approaches for Cognitive Radio Applications
- Source :
- IEEE Access, Vol 8, Pp 128930-128941 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Signal detection, identification, and characterization are among the major challenges in aerial communication systems. The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth. This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency. The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection. Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN). It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16. The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading. High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors. It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shifts.
- Subjects :
- region-based convolutional neural network
region-based convolu-tional neural network
General Computer Science
Computer science
Cyclostationary process
Orthogonal frequency-division multiplexing
02 engineering and technology
symbols.namesake
Signal-to-noise ratio
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Detection theory
Fading
cognitive radio
Wideband
Blind detection
spectrum sensing
business.industry
Bandwidth (signal processing)
Detector
General Engineering
deep learning
020206 networking & telecommunications
Pattern recognition
software-defined communication
wireless communication
Additive white Gaussian noise
Modulation
Frequency domain
symbols
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
Doppler effect
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....590078a37171bd0b826336865737fde8