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| 001 | 679117 | ||
| 005 | 20230408005002.0 | ||
| 024 | 7 | _ | |2 URN |a urn:nbn:de:hbz:82-rwth-2016-109259 |
| 024 | 7 | _ | |2 datacite_doi |a 10.18154/RWTH-2016-10925 |
| 024 | 7 | _ | |2 HBZ |a HT019187585 |
| 024 | 7 | _ | |2 Laufende Nummer |a 35695 |
| 037 | _ | _ | |a RWTH-2016-10925 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 621.3 |
| 100 | 1 | _ | |0 P:(DE-82)058178 |a Bollig, Andreas |b 0 |
| 245 | _ | _ | |a Spectrum sensing in cognitive radio |c vorgelegt von Diplom-Ingenieur Andreas Bollig |h online |
| 260 | _ | _ | |a Aachen |c 2016 |
| 260 | _ | _ | |c 2017 |
| 300 | _ | _ | |a 1 Online-Ressource (vi, 115 Seiten) : Diagramme |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 500 | _ | _ | |a Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2017 |
| 502 | _ | _ | |a Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2016 |b Dissertation |c Rheinisch-Westfälische Technische Hochschule Aachen |d 2016 |g Fak06 |o 2016-10-26 |
| 520 | 3 | _ | |a Zuverlässiges Spectrum Sensing ist die Grundvoraussetzung von opportunistischem Zugriff auf das unterausgelastete Radiospektrum. Die Aufgabe eines Spectrum Sensing Algorithmus ist es, zwischen zwei Hypothesen zu unterscheiden. Der, dass ein beobachtetes spektrales Band frei ist und von einem sekundären System genutzt werden kann (H0), und der, dass das primäre System gerade auf dem Band sendet, so dass das sekundäre System von der Nutzung absehen sollte (H1). Das Ziel im Design von Spectrum Sensing Algorithmen ist es, die Wahrscheinlichkeit eine Übertragung des primären Systems zu detektieren (Detektionswahrscheinlichkeit Pd) bei einer gegebenen festen Wahrscheinlichkeit, das Band fälschlicherweise als belegt einzustufen (Falschalarmwahrscheinlichkeit Pfa), zu maximieren. Im Falle einer verpassten Detektion, also wenn das primäre System sendet, der Detektionsalgorithmus das Band aber als frei einstuft, könnte das sekundäre System eine Übertragung beginnen und damit das primäre System stören. Im Falle eines falschen Alarms verpasst das sekundäre System die Möglichkeit das Spektrum zu nutzen. Diese Arbeit beinhaltet Beiträge zu drei verschiedenen Klassen von Spectrum Sensing Algorithmen: Cyclostationaritätsbasierte Algorithmen, Eigenwertbasierte Algorithmen und Energiedetektoren. |l ger |
| 520 | _ | _ | |a Reliable spectrum sensing is the main enabler for opportunistic access to the underutilized wireless spectrum. The task of a spectrum sensing algorithm is to decide between two hypotheses, the one that the spectral band under observation is free and can be used by a secondary system (H0), and the one that the primary system is transmitting on the band, such that the secondary system needs to refrain from accessing it (H1). The goal in the design of spectrum sensing algorithms is to maximize the probability of detecting a present primary system transmission (probability of detection Pd) given a fixed probability of wrongly determining the band under observation to be occupied when it is not (probability of false alarm Pfa ). In the case of a missed detection, i. e., when the primary system is transmitting but the spectrum sensing algorithm decides that the band is free, the secondary system might also start a transmission, by which it will disturb the primary system. When a false alarm happens, the secondary system misses a chance to use the spectrum. In this thesis, contributions have been made to three types of spectrum sensing algorithms. The first type of spectrum sensing we consider is cyclostationarity detection. Cyclostationarity is a stochastic feature present in all man-made signals, e.g., wireless communication signals, but is absent in pure stationary noise. Due to this property it can be used to decide between H0 and H1, which makes it a good fit for spectrum sensing. The problem arising is that in order to determine the presence or absence of cyclostationarity in a received signal, it has to be known beforehand which cycle frequency is affected. In blind spectrum sensing it is assumed that the secondary system possesses no knowledge about the primary system signal, which, for the above reasons, rules out the use of cyclostationarity. Based on methods from the field of compressed sensing, two algorithms for tackling this problem are proposed. In a second step, a modification of a classic test for cyclostationarity is devised to estimate the test statistic. This modification is necessary to work around the problem that when using the compressed sensing cyclic autocorrelation estimation algorithms, information required for estimating the spectrum sensing test statistic is lost. Furthermore, to assess the cyclic autocorrelation estimation performance of the aforementioned algorithms, a closed-form expression of the discrete-time cyclic autocorrelation of linearly modulated signals with a rectangular pulse shape is derived. Eigenvalue-based spectrum sensing builds on the idea that a communication signal induces either correlation in time or correlation between different receivers, while pure i.i.d. noise does not. The eigenvalues of a received signal’s covariance matrix are used to define various test statistics for spectrum sensing. One of these is the condition number used in the maximum-minimum-eigenvalue (MME) detector. The MME detector is independent of uncertainty regarding the receiver noise power. In contrast, this uncertainty has been shown to lead to an SNR-wall in the energy detector. An SNR-wall constitutes the SNR-value that separates the regime where a detector can robustly detect a primary system signal and the regime where it cannot. Obviously, not exhibiting an SNR-wall is a desired feature of spectrum sensing algorithms. Unfortunately, the MME detector does not possess this feature. Indeed, in this work we show that the MME detector suffers from an SNR-wall induced by uncertainty regarding the amount of coloring of the receiver noise. A lower bound on this SNR-wall is derived and examples for different types of covariance matrices are given. Moreover, it is shown that low amounts of man-made impulsive noise already lead to enough uncertainty in the noise coloring that an SNR-wall considerably far above the desired regime of operation is brought about. Furthermore, two new test statistics for spectrum sensing based on the eigenvalues of the received signal’s covariance matrix are proposed. One of the oldest test statistics used in spectrum sensing is the received signal power. The corresponding method goes by the name of energy detection. It consists of measuring the received energy in a spectral band and comparing it to a predefined threshold. One of the problems occurring in spectrum sensing is the so-called hidden terminal problem, which leads to an SNR between the active node of the primary system and the secondary system sensor that is too low for reliable detection. In order to avoid the problem, a set of spatially distributed sensors is deployed. To exploit the spatial diversity, the sensors have to transmit either a local decision on the spectrum occupancy or their measurement data to a fusion center for combined analysis and decision making. To minimize the resulting overhead in spectrum usage, compressed sensing methods are utilized. Finally, the architecture of the simulation framework used for most numerical evaluations presented in this work is described. It facilitates the reuse of code and benefits its stability. |l eng |
| 591 | _ | _ | |a Germany |
| 653 | _ | 7 | |a spectrum sensing |
| 653 | _ | 7 | |a compressed sensing |
| 653 | _ | 7 | |a eigenvalue-based spectrum sensing |
| 653 | _ | 7 | |a cyclostationarity-based spectrum sensing |
| 653 | _ | 7 | |a energy detection |
| 653 | _ | 7 | |a cognitive radio |
| 700 | 1 | _ | |0 P:(DE-82)IDM00567 |a Mathar, Rudolf |b 1 |e Thesis advisor |u rwth |
| 700 | 1 | _ | |0 P:(DE-82)022260 |a Thomä, Reiner |b 2 |e Thesis advisor |
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