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Blind Vacant Frequency Channels Detection for Cognitive Radio
Wireless P2P Technologies, Research and Development, 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 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 [en]
cognitive radio, energy detection, vacant frequency channels, wavelet transform
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-58205DOI: 10.1109/ICEIC54506.2022.9748704ISI: 000942023400111Scopus ID: 2-s2.0-85128807098ISBN: 9781665409346 (print)OAI: oai:DiVA.org:mdh-58205DiVA, id: diva2:1657502
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
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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