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Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-2419-2735
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7159-7508
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5832-5452
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2019 (English)In: MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), Norfolk, USA: IEEE, 2019, article id 9020735Conference paper, Published paper (Refereed)
Abstract [en]

In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classification speed and accuracy have been studied too.

Place, publisher, year, edition, pages
Norfolk, USA: IEEE, 2019. article id 9020735
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:mdh:diva-52713DOI: 10.1109/MILCOM47813.2019.9020735ISI: 000554849700087Scopus ID: 2-s2.0-85082395256OAI: oai:DiVA.org:mdh-52713DiVA, id: diva2:1502848
Conference
2019 IEEE Military Communications Conference, MILCOM 2019; Norfolk; United States; 12 November 2019 through 14 November 2019; Category numberCFP19MIL-ART; Code 158374
Available from: 2020-11-22 Created: 2020-11-22 Last updated: 2021-03-16Bibliographically approved
In thesis
1. Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio
Open this publication in new window or tab >>Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Abstract. The number of mobile devices is constantly growing, and the exclusivestatic spectrum allocation approach is leading to the spectrum scarcity problem whensome of the licensed bands are heavily occupied and others are nearly unused.Spectrum sharing and opportunistic spectrum access allow achieving more efficientspectrum utilization. Radio scene analysis is a first step in the cognitive radiooperation required to employ opportunistic spectrum access scenarios such as thedynamic spectrum access or frequency hopping spread spectrum. The objective of thiswork is to develop and virtual prototype the subset of radio scene analysis algorithmsintended to be used for deployment of opportunistic spectrum access in our targetapplication: a cognitive radio network consisting of multiple software-defined radionodes BitSDR. The proposed radio scene analysis algorithms are devoted to solvingtwo radio scene analysis problems: 1. detection of vacant frequency channels toimplement spectrum sharing scenarios; 2. waveform estimation including modulationtype, symbol rate, and central frequency estimation. From the subset of two radioscene analysis problems two hypotheses are formulated: the first is related to thevacant band identification and the second to waveform estimation. Then sevenresearch questions related to the trade-off between the sensing accuracy and real-time operation requirement for the proposed radio scene analysis algorithms, the nature of the noise, and assumptions used to model the radio scene environment such as the AWGN channel. In the scope of this work, Hypothesis 1, dedicated to vacant frequency band detection, has been proven. Research questions related to the selection of the observation bandwidth, vacant channels detection threshold, and the optimal algorithm have been answered. We have proposed, prototyped, and tested a vacant frequency channels detection algorithm based on wavelet transform performing multichannel detection in the wide band of 56 MHz based on the received signal observed during500 microseconds. Detection accuracy of 91 % has been demonstrated. Detection has been modeled as a binary hypothesis testing problem. Also, energy detection and cyclostationary feature extraction algorithms have been prototyped and tested, however, they have shown lower classification accuracy than wavelets. Answering research question 7 revealed the advantage of using wavelets due to the potential of the results of wavelet transform to be applied for solving the waveform estimation problem including symbol rate and modulation type. Test data samples have been generated during the controlled experiment by the hardware signal generator and received by proprietary hardware based on AD9364 Analog Devices transceiver. To test Hypothesis 2 research questions related to the waveform estimation have been elaborated. We could not fully prove Hypothesis 2 in the scope of this work. The algorithm and features that have been chosen for modulation type classification have not met the required classification accuracy to classify between five studied modulation classes 2FSK, BPSK, QPSK, 8PSK, and 16PSK. To capture more of the fine differences between the received signal modulated into different linear modulations it has been suggested to use the spectral features derived from the time-series signal observed during 500 microseconds or less observation time in the scope of the future work. However, the binary classification between 2FSK and BPSKpresented in Paper 1 could be performed based on instantaneous values and SNRinput: ensemble boosted trees and decision trees have shown an average classification accuracy of 86.3 % and 86.0 % respectively and classification speed of 1200000objects per second, what is faster than required 2000 objects per second.3The prototyping and testing of the proposed algorithm for symbol rate estimation based on deep learning have been performed to answer research question 2. Wavelet transform feature extraction has been proposed to be applied as a preprocessing step for deep learning-based estimation of the symbol rate for 2FSK modulated signals. This algorithm has shown an improvement in the accuracy of the symbol rate estimation in comparison with cyclostationary based detection. The validation accuracy of the symbol rate classification has reached 99.7 %. During testing, the highest average classification accuracy of 100 % has been observed for the signals with SNR levels 25-30 dB, while for signals with SNR 20-25 dB it was 96.3 %.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 300
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-52881 (URN)978-91-7485-493-0 (ISBN)
Presentation
2021-01-22, U2-024 and virtually via Zoom, Mälardalens högskola, Västerås, 09:15 (English)
Supervisors
Available from: 2020-12-31 Created: 2020-12-21 Last updated: 2022-11-08Bibliographically approved

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Valieva, InnaBjörkman, MatsÅkerberg, JohanEkström, Mikael

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