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Development of Self-Organizing Methods for Radio Spectrum Sensing

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Development of Self-Organizing Methods for Radio Spectrum Sensing VILNIUS GEDIMINAS TECHNICAL UNIVERSITY Liudas STAŠIONIS DEVELOPMENT OF SELF-ORGANIZING METHODS FOR RADIO SPECTRUM SENSING DOCTORAL DISSERTATION TECHNOLOGICAL SCIENCES, ELECTRICAL AND ELECTRONIC ENGINEERING (01T) Vilnius 2016 Doctoral dissertation was prepared at Vilnius Gediminas Technical University in 2011–2016. Scientific supervisor Assoc Prof Dr Artūras SERACKIS (Vilnius Gediminas Technical University, Electrical and Electronic Engineering – 01T). The Dissertation Defense Council of Scientific Field of Electrical and Electronic Engineering of Vilnius Gediminas Technical University: Chairman Prof Dr. Šarūnas PAULIKAS (Vilnius Gediminas Technical University, Electrical and Electronic Engineering – 01T). Members: Prof Dr Habil Gintautas DZEMYDA (Vilnius University, Informatics Engineering – 07T), Assoc Prof Dr Rimantas PUPEIKIS (Vilnius Gediminas Technical University, Informatics – 09P), Dr Habil Arūnas Vytautas TAMAŠEVIČIUS (State research institute Center for Physical Sciences and Technology, Electrical and Electronic Engineering – 01T), Dr Habil Yevhen YASHCHYSHYN (Warsaw University of Technology, Electrical and Electronic Engineering – 01T). The dissertation will be defended at the public meeting of the Dissertation Defense Council of Electrical and Electronics Engineering in the Senate Hall of Vilnius Gediminas Technical University at 9 a. m. on 29 April 2016. Address: Saulėtekio al. 11, LT-10223 Vilnius, Lithuania. Tel.: +370 5 274 4956; fax +370 5 270 0112; e-mail: [email protected] A notification on the intend defending of the dissertation was send on 25 March 2016. A copy of the doctoral dissertation is available for review at the VGTU repository: http://dspace.vgtu.lt/ and at the Library of Vilnius Gediminas Technical University (Saulėtekio al. 14, LT-10223 Vilnius, Lithuania). VGTU leidyklos TECHNIKA 2363-M mokslo literatūros knyga . ISBN 978-609-457-912-7 © VGTU leidykla TECHNIKA, 2016 © Liudas Stašionis, 2016 [email protected] VILNIAUS GEDIMINO TECHNIKOS UNIVERSITETAS Liudas STAŠIONIS SAVIORGANIZUOJANČIŲ METODŲ RADIJO DAŽNIŲ JUOSTOS UŽIMTUMUI STEBĖTI KŪRIMAS DAKTARO DISERTACIJA TECHNOLOGIJOS MOKSLAI, ELEKTROS IR ELEKTRONIKOS INŽINERIJA (01T) Vilnius 2016 Disertacija rengta 2011–2016 metais Vilniaus Gedimino technikos universitete. Vadovas doc. dr. Artūras SERACKIS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija – 01T). Vilniaus Gedimino technikos universiteto Elektros ir elektronikos inžinerijos mokslo krypties disertacijos ginimo taryba: Pirmininkas prof. dr. Šarūnas PAULIKAS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija – 01T). Nariai: prof. habil. dr. Gintautas DZEMYDA (Vilniaus universitetas, informatikos inžinerija – 07T), habil. dr. Yevhen YASHCHYSHYN (Varšuvos technologijos universitetas, elektros ir elektronikos inžinerija – 01T), doc. dr. Rimantas PUPEIKIS (Vilniaus Gedimino technikos universitetas, informatika – 09P), habil. dr. Arūnas Vytautas TAMAŠEVIČIUS (Valstybinis mokslinių tyrimų institutas Fizinių ir technologijos mokslų centras, elektros ir elektronikos inžinerija – 01T). Disertacija bus ginama Elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2016 m. balandžio 29 d. 9 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje. Adresas: Saulėtekio al. 11, LT-10223 Vilnius, Lietuva. Tel.: (8 5) 274 4956; faksas (8 5) 270 0112; el. paštas [email protected] Pranešimai apie numatomą ginti disertaciją išsiųsti 2016 m. kovo 25 d. Disertaciją galima peržiūrėti VGTU talpykloje http://dspace.vgtu.lt ir Vilniaus Ge- dimino technikos universiteto bibliotekoje (Saulėtekio al. 14, LT-10223 Vilnius, Lietuva). Abstract A problem of wide-band radio spectrum analysis in real time was solved and presented in the dissertation. The goal of the work was to develop a spec- trum sensing method for primary user emission detection in radio spectrum by investigating new signal feature extraction and intelligent decision making techniques. A solution of this problem is important for application in cognitive radio systems, where radio spectrum is analyzed in real time. In thesis there are reviewed currently suggested spectrum analysis meth- ods, which are used for cognitive radio. The main purpose of these methods is to optimize spectrum description feature estimation in real-time systems and to select suitable classification threshold. For signal spectrum description ana- lyzed methods used signal energy estimation, analyzed energy statistical differ- ence in time and frequency. In addition, the review has shown that the wavelet transform can be used for signal pre-processing in spectrum sensors. For clas- sification threshold selection in literature most common methods are based on statistical noise estimate and energy statistical change analysis. However, there are no suggested efficient methods, which let classification threshold to change adaptively, when RF environment changes. It were suggested signal features estimation modifications, which let to increase the efficiency of algorithm implementation in embedded system, by decreasing the amount of required calculations and preserving the accuracy of spectrum analysis algorithms. For primary signal processing it is suggested to use wavelet transform based features extraction, which are used for spectrum sensors and lets to in- crease accuracy of noisy signal detection. All primary user signal emissions were detected with lower than 1% false alarm ratio. In dissertation, there are suggested artificial neural network based methods, which let adaptively select classification threshold for the spectrum sensors. During experimental tests, there was achieved full signals emissions detection with false alarm ratio lower than 1%. It was suggested self organizing map structure modification, which in- creases network self-training speed up to 32 times. This self-training speed is achieved due to additional inner weights, which are added in to self organiz- ing map structure. In self-training stage network structure changes especially fast and when topology, which is suited for given task, is reached, in further self-training iterations it can be disordered. In order to avoid this over-training, self-training process monitoring algorithms must be used. There were sug- gested original methods for self-training process control, which let to avoid network over-training and decrease self-training iteration quantity. v Reziumė Disertacijoje sprendžiama plačios juostos radijo spektro analizės realiuoju laiku problema. Analizės metu siekiama spektre nustatyti sritis (juostas, kanalus) kuriuose nėra jokio pirminio vartotojo (angl. Primary User) signalo ir nuolat stebėti, ar analizuojamoje srityje neatsiranda nežinomas pirminio vartotojo sig- nalas. Tokio pobūdžio problemos sprendžiamos kognityvinio radijo uždavini- uose, kai radijo dažnių ruožas analizuojamas realiuoju laiku. Disertacijoje apžvelgti šiuo metu siūlomi signalo spektro analizės me- todai, skirti kognityviniam radijui. Šių metodų pagrindiniai iššūkiai – op- timizuoti signalo spektro apibūdinimui naudojamų požymių skaičiavimą re- aliojo laiko sistemose ir parinkti tinkamą signalų klasifikatorius slenkstį. Sig- nalų spektrui apibūdinti naudojamas signalo energijos skaičiavimas, analizuo- jamos statistinės energijos pokyčio laike savybės. Taip pat gali būti taiko- mas ir vilnelių transformacija grįstas signalų pirminis apdorojimas. Klasi- fikatoriaus slenksčiui parinkti literatūroje sutinkami metodai, grįsti statistiniais triukšmo įverčiais, energijos pokyčio analize, tačiau nėra pasiūlytų efektyvių metodų, leidžiančių klasifikatoriaus slenksčio reikšmes keisti adaptyviai, pak- itus triukšmo lygiui analizuojamoje aplinkoje. Buvo pasiūlytos signalų požymių skaičiavimo patobulinimai, leidžiančios padidinti įgyvendinimo įterptinėje sistemoje efektyvumą, sumažinant reikiamų atlikti skaičiavimų apimtis, ir išlaikyti šiuos požymius naudojančio spektro analizės algoritmo efektyvumą. Pirminiam signalų apdorojimui pasiūlyti nauji vilnelių transformacija grįsti požymiai, skirti taikyti spektro jutikliuose ir leidžiantys padidinti signalų at- pažinimo triukšme tikslumą. Naudojantis šiais požymiais pirminio vartotojo signalo visos emisijos aptiktos su mažesniu nei 1% klaidos santykiu. Pasiūlyti saviorganizuojančių neuronų (Kohonen) tinklų mokymusi grįsti metodai, lei- džiantys spektro jutiklyje adaptyviai parinkti klasifikatoriaus slenkstį. Tinklų eksperimentuose pirminio vartotojo visos emisijos aptiktos su mažesniu nei 1% klaidos santykiu. Pasiūlyta saviorganizuojančio neuronų tinklo struktūros patobulinimai, lei- džianti paspartinti tinklo mokymąsi iki 32 kartų. Tokia tinklo mokymosi sparta pasiekta dėl papildomų vidinių ryšių, kuriais buvo papildyta Kohonen tinklo struktūra. Mokymosi metu tinklo struktūra kinta ypač sparčiai ir pasiekusi topologiją, tinkamą iškeltam uždaviniui spręsti, tolesnio mokymosi metu šią topologiją gali išardyti. Siekiant išvengti tokio Kohonen tinklo persimokymo, reikia taikyti mokymo eigos stebėjimo algoritmus. Pasiūlyti originalūs me- todai saviorganizuojančio neuronų tinklo mokymui sustabdyti, leidžiantys iš- vengti tinklo persimokymo ir sumažinti mokymui skirtų iteracijų skaičių. vi Notations Symbols α log-sigmoid activation function slope coefficient; bij ij neuron bias; W Cf spectral coherence coefficient estimated in W frequency window; D variance; Dk modified variance, when k: N – variance without FIFO, M – variance without square operation; D desired network output; bij ij neuron bias; dij distance
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