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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
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
Electronics Development, Wireless P2P Technologies, Sweden.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-2419-2735
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-7159-7508
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2021 (Engelska)Ingår i: Advances in Science, Technology and Engineering Systems, ISSN 2415-6698, Vol. 6, nr 1, s. 658-671Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
ASTES Publishers , 2021. Vol. 6, nr 1, s. 658-671
Nyckelord [en]
Cognitive radio, Machine learning, Modulation classification
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:mdh:diva-53579DOI: 10.25046/aj060172Scopus ID: 2-s2.0-85101023812OAI: oai:DiVA.org:mdh-53579DiVA, id: diva2:1534421
Anmärkning

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

Tillgänglig från: 2021-03-05 Skapad: 2021-03-05 Senast uppdaterad: 2023-03-10Bibliografiskt granskad
Ingår i avhandling
1. Spectrum Sensing for Cognitive Radio
Öppna denna publikation i ny flik eller fönster >>Spectrum Sensing for Cognitive Radio
2023 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalens universitet, 2023
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 373
Nationell ämneskategori
Teknik och teknologier Elektroteknik och elektronik
Forskningsämne
elektronik
Identifikatorer
urn:nbn:se:mdh:diva-61898 (URN)978-91-7485-583-8 (ISBN)
Disputation
2023-04-12, Gamma, Mälardalens universitet, Västerås, 09:15 (Engelska)
Opponent
Tillgänglig från: 2023-02-14 Skapad: 2023-02-13 Senast uppdaterad: 2023-03-22Bibliografiskt granskad

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Valieva, InnaBjörkman, MatsÅkerberg, JohanEkström, Mikael

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