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Frequency Bands Classification Using Machine Learning for Frequency Hopping Spread Spectrum
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-2419-2735
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-7159-7508
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(engelsk)Manuskript (preprint) (Annet vitenskapelig)
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Identifikatorer
URN: urn:nbn:se:mdh:diva-62054OAI: oai:DiVA.org:mdh-62054DiVA, id: diva2:1742733
Tilgjengelig fra: 2023-03-10 Laget: 2023-03-10 Sist oppdatert: 2023-03-10bibliografisk kontrollert
Inngår i avhandling
1. Spectrum Sensing for Cognitive Radio
Åpne denne publikasjonen i ny fane eller vindu >>Spectrum Sensing for Cognitive Radio
2023 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalens universitet, 2023
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 373
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Forskningsprogram
elektronik
Identifikatorer
urn:nbn:se:mdh:diva-61898 (URN)978-91-7485-583-8 (ISBN)
Disputas
2023-04-12, Gamma, Mälardalens universitet, Västerås, 09:15 (engelsk)
Opponent
Tilgjengelig fra: 2023-02-14 Laget: 2023-02-13 Sist oppdatert: 2023-03-22bibliografisk kontrollert

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Valieva, InnaBjörkman, MatsÅkerberg, JohanEkström, Mikael

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