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Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
2020 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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 %.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University , 2020.
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 300
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:mdh:diva-52881ISBN: 978-91-7485-493-0 (tryckt)OAI: oai:DiVA.org:mdh-52881DiVA, id: diva2:1511883
Presentation
2021-01-22, U2-024 and virtually via Zoom, Mälardalens högskola, Västerås, 09:15 (Engelska)
Handledare
Tillgänglig från: 2020-12-31 Skapad: 2020-12-21 Senast uppdaterad: 2022-11-08Bibliografiskt granskad
Delarbeten
1. Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
Öppna denna publikation i ny flik eller fönster >>Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
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2019 (Engelska)Ingår i: MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), Norfolk, USA: IEEE, 2019, artikel-id 9020735Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Norfolk, USA: IEEE, 2019
Nationell ämneskategori
Telekommunikation
Identifikatorer
urn:nbn:se:mdh:diva-52713 (URN)10.1109/MILCOM47813.2019.9020735 (DOI)000554849700087 ()2-s2.0-85082395256 (Scopus ID)
Konferens
2019 IEEE Military Communications Conference, MILCOM 2019; Norfolk; United States; 12 November 2019 through 14 November 2019; Category numberCFP19MIL-ART; Code 158374
Tillgänglig från: 2020-11-22 Skapad: 2020-11-22 Senast uppdaterad: 2021-03-16Bibliografiskt granskad
2. Blind symbol rate estimation for cognitive radio using wavelet transform and deep learning for fsk modulated digital signals
Öppna denna publikation i ny flik eller fönster >>Blind symbol rate estimation for cognitive radio using wavelet transform and deep learning for fsk modulated digital signals
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(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Abstract [en]

This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals. Symbol rate estimation is based on the classification between three symbol rate classes: 10, 100 and 1000K Symbol/second using the scalogram images obtained from time-frequency analysis performed using the 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 from 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. 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. Proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy especially in conditions of low SNR. Central frequency estimation has been performed using a modified periodogram estimate of the power spectral density with a rectangular window. 

Nationell ämneskategori
Telekommunikation
Identifikatorer
urn:nbn:se:mdh:diva-52715 (URN)
Tillgänglig från: 2020-11-22 Skapad: 2020-11-22 Senast uppdaterad: 2022-12-07Bibliografiskt granskad
3. AUTONOMOUS DETECTION OF VACANT FREQUENCY BANDS FOR COGNITIVE RADIO
Öppna denna publikation i ny flik eller fönster >>AUTONOMOUS DETECTION OF VACANT FREQUENCY BANDS FOR COGNITIVE RADIO
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(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Abstract [en]

This This paper is focused on thea utonomous detection of the vacant frequency channels in the wide observation band of 60MHz. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms including energy detection, wavelets, and cyclostationary have been tested and evaluated in terms of accuracy.Testing has been performed offline on the data samples collected during the controlled experiment. Data samples consisting of AWGN noise and FSK, BPSK, QPSK modulated signals have been generated using thehardware signal generator and received on our targetapplication's receiver (AD9364) front end as a time domain complex signal. The optimal threshold value hasbeen determined as an optimal value between the hitrate and the false positive rate. The highest accuracy of 91.0% has been reached the wavelet transform feature extraction, energy detection has shown 86.4% accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculatedfor the AWGN noise sample and samples containing BPSK and 2FSK modulated signals captured with -20dB power.

Nationell ämneskategori
Telekommunikation
Identifikatorer
urn:nbn:se:mdh:diva-52714 (URN)
Tillgänglig från: 2020-11-22 Skapad: 2020-11-22 Senast uppdaterad: 2021-03-16Bibliografiskt granskad

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