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Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Research and Development, Wireless P2P Technologies, Falun, 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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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.

Place, publisher, year, edition, pages
2022.
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:mdh:diva-61136DOI: 10.1109/atc55345.2022.9943051Scopus ID: 2-s2.0-85142741268OAI: oai:DiVA.org:mdh-61136DiVA, id: diva2:1716918
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
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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