mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
Mälardalen University.
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
Show others and affiliations
2019 (English)In: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 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
Institute of Electrical and Electronics Engineers Inc. , 2019. article id 9020735
Keywords [en]
machine learning, modulation classification, signal-to-noise ratio, software defined radio, Analog circuits, Cognitive radio, Decision trees, Digital radio, Forestry, Learning algorithms, Learning systems, Military communications, Modulation, Nearest neighbor search, Radio, Radio transceivers, Signal to noise ratio, Software radio, Support vector machines, Classification accuracy, Classification features, Digital modulations, Modulation type classification, Quadrature components, Software-defined radios, Supervised machine learning, Classification (of information)
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-47459DOI: 10.1109/MILCOM47813.2019.9020735Scopus ID: 2-s2.0-85082395256ISBN: 9781728142807 (print)OAI: oai:DiVA.org:mdh-47459DiVA, id: diva2:1421159
Conference
2019 IEEE Military Communications Conference, MILCOM 2019, 12 November 2019 through 14 November 2019
Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-04-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Björkman, MatsÅkerberg, JohanEkström, Mikael

Search in DiVA

By author/editor
Valieva, InnaBjörkman, MatsÅkerberg, JohanEkström, Mikael
By organisation
Mälardalen UniversityEmbedded Systems
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf