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Multiple Machine Learning Algorithms Comparison for Coarse Frequency Bands Classification
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
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:mdh:diva-62053OAI: oai:DiVA.org:mdh-62053DiVA, id: diva2:1742731
Tillgänglig från: 2023-03-10 Skapad: 2023-03-10 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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