<p>To be able to evaluate different radio waveforms in a laboratory environment FOI in Linköping has developed an experimental system for radio communication called Rasmus. The system consists of a transmitter, a channel emulator and a receiver. The transmitter modulates a digital signal to a desired analogue waveform. The channel emulator is used to simulate a real radio channel. The channel emulator distorts the signal by splitting it up in several propagation paths. Each path has its own statistical distribution, delay and so on. The receiver digitises the signal and saves it in a memory. Analysis of the signal is performed in non-real time. The system has some limitations concerning signal bandwidths and channel models. </p><p>This Master Thesis deals with the problem of scaling the radio channel (with respect to noise, Doppler shift and fading) in order to examine signals that have properties outside the normal operation of the Rasmus system. </p><p>The radio channel studied is a Rayleigh fading channel with two independent propagation paths. A differentially modulated QPSK signal is used as input to the channel. The signal is filtered through root-raised-cosine filter in the transmitter and receiver to reduce the effect of inter-symbol interference. </p><p>Theoretical calculations result in a method, that describe which parameters that should be scaled and how they should be scaled. Simulations are carried out in order to verify the method.</p>

To be able to evaluate different radio waveforms in a laboratory environment FOI in Linköping has developed an experimental system for radio communication called Rasmus. The system consists of a transmitter, a channel emulator and a receiver. The transmitter modulates a digital signal to a desired analogue waveform. The channel emulator is used to simulate a real radio channel. The channel emulator distorts the signal by splitting it up in several propagation paths. Each path has its own statistical distribution, delay and so on. The receiver digitises the signal and saves it in a memory. Analysis of the signal is performed in non-real time. The system has some limitations concerning signal bandwidths and channel models. This Master Thesis deals with the problem of scaling the radio channel (with respect to noise, Doppler shift and fading) in order to examine signals that have properties outside the normal operation of the Rasmus system. The radio channel studied is a Rayleigh fading channel with two independent propagation paths. A differentially modulated QPSK signal is used as input to the channel. The signal is filtered through root-raised-cosine filter in the transmitter and receiver to reduce the effect of inter-symbol interference. Theoretical calculations result in a method, that describe which parameters that should be scaled and how they should be scaled. Simulations are carried out in order to verify the method.

In large antenna systems, traditional channel estimation is costly and infeasible in some situations. Compressive sensing was proposed to estimate the channel with fewer measurements. Most of the previous work uses a predefined discrete Fourier transform matrix or overcomplete Fourier transform matrix to approximate the channel. Then, a learned dictionary trained by K-singular value decomposition (K-SVD) was proposed and was proved superiority using orthogonal matching pursuit (OMP) to reconstruct the sparse channel. However, with the development of compressive sensing, there are plenty of dictionary learning algorithms and sparse recovery algorithms. It is important to identify the effect and the performance of different algorithms when transforming the high dimensional channel vectors to low dimensional representations. In this thesis, we use a spatial channel model to generate channel vectors. Dictionaries are trained by K-SVD and method of optimal directions (MOD). Several sparse recovery algorithms are used to find the sparse approximation of the channel like OMP and gradient descent with sparsification (GraDeS). We present simulation results and discuss the performance of the various algorithms in terms of accuracy, sparsity, and complexity. We find that predefined dictionaries works with most of the algorithms in sparse recovery but learned dictionaries only work with pursuit algorithms, and only show superiority when the algorithm coincides with the algorithm in the sparse coding stage. / I stora antennsystem är traditionell kanaluppskattning kostsam och omöjlig i vissa situationer. Kompressionsavkänning föreslogs för att uppskatta kanalen med färre mätningar. Det mesta av det tidigare arbetet använder en fördefinierad diskret Fourier transformmatris eller överkompletterad Fourier -transformmatris för att approximera kanalen. Därefter föreslogs en inlärd ordbok som utbildats av K-SVD och bevisades överlägsen med hjälp av OMP för att rekonstruera den glesa kanalen. Men med utvecklingen av komprimerad avkänning finns det gott om algoritmer för inlärning av ordlistor och glesa återställningsalgoritmer. Det är viktigt att identifiera effekten och prestandan hos olika algoritmer när de högdimensionella kanalvektorerna omvandlas till lågdimensionella representationer. I denna avhandling använder vi en rumslig kanalmodell för att generera kanalvektorer. Ordböcker tränas av K-SVD och MOD. Flera glesa återställningsalgoritmer används för att hitta den glesa approximationen av kanalen som OMP och GraDeS. Vi presenterar simuleringsresultat och diskuterar prestanda för de olika algoritmerna när det gäller noggrannhet, sparsamhet och komplexitet. Vi finner att fördefinierade ordböcker fungerar med de flesta algoritmerna i gles återhämtning, men inlärda ordböcker fungerar bara med jaktalgoritmer och visar bara överlägsenhet när algoritmen sammanfaller med algoritmen i det glesa kodningsstadiet.

In der vorliegenden Arbeit wird eine Synthese-Vorauslegungsmethode beschrieben, mit der für individuelle Anforderungsprofile in einem frühen Entwicklungsprozess eine methodische Generierung von Lösungsvarianten für Polymerelektrolytmembran-Brennstoffzellen-Stacks erfolgt. Hierzu werden Parametervariationen auf Basis Kanalquerschnitts- und Flussfeldmodellen durchgeführt. Weiterhin werden Berechnungsmodule genutzt, um individuelle konstruktive und verfahrenstechnische Kennwerte der jeweiligen Lösungsvarianten zu bestimmen, unter anderem hinsichtlich GDL-Intrusionseffekten, hydraulischer Druckabfälle, der Umformschädigung metallischer Kanalstrukturen sowie der Berechnung der Flussfeldüberdeckungsbereiche zweier kombinierter Flussfelder. Auf Basis der Variantenkennwerte erfolgen wichtungsbasierte Bewertungsprozesse, aus denen gesamtheitliche Vorzugslösungen resultieren. In einem Beispielszenario wird die Anwendung der präsentierten Synthese-Vorauslegungsmethode exemplarisch dargestellt sowie die experimentellen Ergebnisse zur Charakterisierung der real umgesetzten Vorzugslösung aufgezeigt.:1 Einleitung und Motivation 2 Stand der Wissenschaft und Technik 3 Grundlagen von PEM-BZ 4 Synthese-Auslegungsmethode 5 Berechnungsmodule 6 Anwendung der Synthese-Methode 7 Schlussbetrachtungen