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Multiple machine learning algorithms comparison for modulation type classification based on instantaneous values of the time domain signal and time series statistics derived from wavelet transform
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Electronics Development, Wireless P2P Technologies, Sweden.
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
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2021 (English)In: Advances in Science, Technology and Engineering Systems, ISSN 2415-6698, Vol. 6, no 1, p. 658-671Article in journal (Refereed) Published
Abstract [en]

Modulation type classification is a part of waveform estimation required to employ spectrum sharing scenarios like dynamic spectrum access that allow more efficient spectrum utilization. In this work multiple classification features, feature extraction, and classification algorithms for modulation type classification have been studied and compared in terms of classification speed and accuracy to suggest the optimal algorithm for deployment on our target application hardware. The training and validation of the machine learning classifiers have been performed using artificial data. The possibility to use instantaneous values of the time domain signal has shown acceptable performance for the binary classification between BPSK and 2FSK: Both ensemble boosted trees with 30 decision trees learners trained using AdaBoost sampling and fine decision trees have shown optimal performance in terms of both an average classification accuracy (86.3 % and 86.0 %) and classification speed (120 0000 objects per second) for additive white gaussian noise (AWGN) channel with signal-to-noise ratio (SNR) ranging between 1 and 30 dB. However, for the classification between five modulation classes demonstrated average classification accuracy has reached only 78.1 % in validation. Statistical features: Mean, Standard Deviation, Kurtosis, Skewness, Median Absolute Deviation, Root-Mean-Square level, Zero Crossing Rate, Interquartile Range and 75th Percentile derived from the wavelet transform of the received signal observed during 100 and 500 microseconds were studied using fractional factorial design to determine the features with the highest effect on the response variables: classification accuracy and speed for the additive white gaussian noise and Rician line of sight multipath channel. The highest classification speed of 170 000 objects/second and 100 % classification accuracy has been demonstrated by fine decision trees using as an input Kurtosis derived from the wavelet coefficients derived from signal observed during 100 microseconds for AWGN channel. For the line of sight fading Rician channel with AWGN demonstrated classification speed is slower 130 000 objects/s.

Place, publisher, year, edition, pages
ASTES Publishers , 2021. Vol. 6, no 1, p. 658-671
Keywords [en]
Cognitive radio, Machine learning, Modulation classification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-53579DOI: 10.25046/aj060172Scopus ID: 2-s2.0-85101023812OAI: oai:DiVA.org:mdh-53579DiVA, id: diva2:1534421
Note

Export Date: 5 March 2021; Article; Correspondence Address: Valieva, I.; School of Innovation, Sweden; email: inna.valieva@mdh.se

Available from: 2021-03-05 Created: 2021-03-05 Last updated: 2023-03-10Bibliographically approved
In thesis
1. Spectrum Sensing for Cognitive Radio
Open this publication in new window or tab >>Spectrum Sensing for Cognitive Radio
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This work focuses on the improvement of spectrum utilization by evaluating and proposing a subset of radio scene analysis algorithms for opportunistic spectrum access deployment in a cognitive radio network. The proposed algorithms aim to solve two problems: detecting vacant frequency channels and estimating the waveform, including modulation type, symbol rate, and central frequency. To test and prove the hypothesis three research questions related to radio scene observation, classification, and estimation have been formulated, studied, and answered. A two-step spectrum sensing algorithm has been proposed. The first step covers the coarse classification of the observed band into three broad categories: white, gray, or black space, commonly used in the literature to describe spectrum occupancy. Various machine learning algorithms were applied and tested for the coarse classification step. Fine decision trees demonstrated the highest classification accuracy and speed. The second step covers the detailed gray space analysis performed to detect vacant channels and waveforms of the signals present in the observed band. Algorithms such as cyclostationary, energy detection, and wavelet transform were employed for solving the vacant channel detection. The hypothesis has been proven by demonstrating the possibility of blind real-time vacant frequency channel detection using discrete wavelet transform and energy detection within the time compatible with real-time operation and 5G latency requirements on the test hardware.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 373
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-61898 (URN)978-91-7485-583-8 (ISBN)
Public defence
2023-04-12, Gamma, Mälardalens universitet, Västerås, 09:15 (English)
Opponent
Available from: 2023-02-14 Created: 2023-02-13 Last updated: 2023-03-22Bibliographically approved

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Valieva, InnaBjörkman, MatsÅkerberg, JohanEkström, Mikael

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