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Spectrum Sensing for Cognitive Radio
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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: urn:nbn:se:mdh:diva-61898ISBN: 978-91-7485-583-8 (print)OAI: oai:DiVA.org:mdh-61898DiVA, id: diva2:1736525
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
List of papers
1. 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
Open this publication in new window or tab >>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
Show others...
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
Keywords
Cognitive radio, Machine learning, Modulation classification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-53579 (URN)10.25046/aj060172 (DOI)2-s2.0-85101023812 (Scopus ID)
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
2. Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals
Open this publication in new window or tab >>Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals
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2022 (English)In: 2022 International Conference on Advanced Technologies for Communications (ATC), 2022Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals, based on the classification between three symbol rate classes: 10, 100, and 1000 KSymbol/second using the scalogram images obtained from continuous wavelet transform with Morse wavelet. Pretrained deep learning AlexNet has been transfer learned to classify between symbol rate classes. Training, testing, and validation data sets have been composed of the artificial data generated using Bernoulli binary random signal generator modulated into FSK signal corrupted by additive white Gaussian noise (AWGN) noise with SNR ranging from 1 to 30 dB. Training and validation data sets have been augmented to obtain twice more extensive data set i.e 1800 scalogram images, compared to the original size of 900 samples. The average classification accuracy during validation has reached 99.7 % and during testing 100 % and 96.3 % for the data sets with SNR 25–30 dB and 20–25 dB respectively. The proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy, especially in conditions of low SNR.

National Category
Telecommunications
Identifiers
urn:nbn:se:mdh:diva-61136 (URN)10.1109/atc55345.2022.9943051 (DOI)2-s2.0-85142741268 (Scopus ID)
Conference
2022 International Conference on Advanced Technologies for Communications (ATC), 20-22 October 2022
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-03-10Bibliographically approved
3. Blind Vacant Frequency Channels Detection for Cognitive Radio
Open this publication in new window or tab >>Blind Vacant Frequency Channels Detection for Cognitive Radio
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2022 (English)In: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the blind vacant frequency channels detection in 56 MHz observation band divided into 56 channels (1 MHz each) implemented on in-house developed hardware based on the AD9364 transceiver operating in automatic gain control (AGC) mode. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms widely used in the literature including energy detection, wavelets, and cyclostationary have been tested and evaluated for potential use in our target application. Primary, offline testing has been performed in the Matlab environment using the data samples captured on the target receiver's front end as a time-domain complex signal. Data samples containing one, two, or three signals generated by hardware signal generator and modulated into 2FSK, BPSK, or QPSK with symbol rate 10, 100, or 1000 kSymbol/s. The highest accuracy of 91.0 % has been observed in the offline detection for continuous wavelet transform, while energy detection has demonstrated 86.4 % accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculated for the AWGN noise sample and the sample containing BPSK and 2FSK modulated signals. Energy detection and discrete wavelet transform have been implemented on our target hardware and tested in the office environment in conditions that could be approximated by AWGN channel. Test sequences containing one or two signals have been generated by the signal generator and received and processed by our target radio node. Discrete wavelet transform has demonstrated 85.73 % and energy detection 85.25 % accuracy in real-time testing. © 2022 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
cognitive radio, energy detection, vacant frequency channels, wavelet transform
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-58205 (URN)10.1109/ICEIC54506.2022.9748704 (DOI)000942023400111 ()2-s2.0-85128807098 (Scopus ID)9781665409346 (ISBN)
Conference
2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Available from: 2022-05-11 Created: 2022-05-11 Last updated: 2023-03-22Bibliographically approved
4. 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
Open this publication in new window or tab >>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
Show others...
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
Keywords
Cognitive radio, Machine learning, Modulation classification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-53579 (URN)10.25046/aj060172 (DOI)2-s2.0-85101023812 (Scopus ID)
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
5. Multiple Machine Learning Algorithms Comparison for Coarse Frequency Bands Classification
Open this publication in new window or tab >>Multiple Machine Learning Algorithms Comparison for Coarse Frequency Bands Classification
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-62053 (URN)
Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-03-10Bibliographically approved
6. Frequency Bands Classification Using Machine Learning for Frequency Hopping Spread Spectrum
Open this publication in new window or tab >>Frequency Bands Classification Using Machine Learning for Frequency Hopping Spread Spectrum
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-62054 (URN)
Available from: 2023-03-10 Created: 2023-03-10 Last updated: 2023-03-10Bibliographically approved

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Citation style
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More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf