The goal of software-defined radio (SDR) is to move the processing of radio signals from the analog domain to the digital domain — to use digital microchips instead of analog circuit components. Until faster, higher-precision analog-to-digital (ADCs) and digital-to-analog converters (DACs) become affordable, however, some analog signal processing will be necessary. We still need to convert high-radio frequency (RF) signals that we receive to low intermediate-frequency (IF) or baseband (centered on zero Hz) signals in order for ADCs to sample them and feed them into microchips for processing. The reverse is true when we transmit. Amplification is also needed on the receive side to fully utilize the dynamic range of the ADC and power amplification is needed on the transmit side to increase the power output from the DAC for transmission. Analog filtering is also needed to avoid saturating the ADC or to filter out interference when receiving and to avoid transmitting spurs. The analog frequency conversion, amplification and filtering section of a radio is called the RF front end. This thesis describes work on a new RF front end daughterboard for the Universal Software Radio Peripheral, or USRP. The USRP is a software-radio hardware platform designed to be used with the GNU Radio software radio software package. Using the Motorola RFIC4 chip, the new daughterboard receives RF signals, converts them to baseband and does analog filtering and amplification before feeding the signal into the USRP for processing. The chip also takes transmit signals from the USRP, converts them from baseband to RF and amplifies and filters them. The board was designed and laid out by Randall Nealy. I wrote the software driver for GNU Radio. The driver defines the interface between the USRP and the RFIC chip, controls the physical settings, and calculates and sets the hundreds of variables necessary to operate this extremely complex chip correctly. It allows plug-and-play compatibility with the current USRP daughterboards and supplies additional functions not available in any other daughterboard. / Master of Science

In dieser Bachelorarbeit wird eine LTE Cell Search Procedure auf Basis von SDR entwickelt, um eine Synchronisation mit einem LTE-System in Zeit und Frequenz zu erreichen. Dabei werden mehrere Verarbeitungsblöcke implementiert, die zur LTE Cell Search Procedure gehören. Diese beinhalten die Erkennung des Integer Frequency Offsets (IFO), Primary Synchronisation Signals (PSS), Fractional Frequency Offset (FFO) und des Secondary Synchronisation Signals (SSS). Die Arbeit umfasst eine Literaturrecherche, die Implementierung der Verarbeitungsblöcke, die Simulation verschiedener Szenarien, statistische Analysen und die Anwendung auf ein gemessenes LTE-Signal. Sie liefert Antworten auf spezifische Forschungsfragen zur Effizienz, zum Einfluss des SNR und des CFO, sowie zur Eignung für reale LTE-Systeme. Darüber hinaus wird ein Ausblick auf mögliche Anwendungen im Kontext von 5G NR, WLAN und Erweiterungen gegeben.:Kurzfassung.................................... III Abbildungsverzeichnis .............................. VII Tabellenverzeichnis................................ VIII Codeverzeichnis.................................. IX Abkürzungsverzeichnis .............................. X Symbolverzeichnis ................................ XIV 1. Einleitung................................... 1 1.1. Forschungsfragen ............................ 1 1.2. Untersuchungsdesign .......................... 2 2. Grundlagen .................................. 3 2.1. Zadoff-Chu (ZC)-Sequenzen ...................... 3 2.2. Maximum Length (M)-Sequenzen................... 5 2.3. Orthogonal Frequency-Division Multiplexing (OFDM) ................ 7 2.4. Orthogonal Frequency-Division Multiplexing (OFDM): Kanal und Equalization............................... 10 2.5. Orthogonal Frequency-Division Multiplexing (OFDM): Carrier Frequency Offset (CFO).......................... 11 2.6. Carrier Frequency Offset (CFO)-Erkennung ................... 14 2.7. Short Time Fourier Transform (STFT) und Spektrogramm ......... 16 2.8. Software Defined Radio (SDR) .................... 17 2.9. 3rd Generation Partnership Project (3GPP)-Long Term Evolution (LTE).................................. 18 3. Praktische Untersuchung ........................... 23 3.1. Simulationsumgebung ......................... 27 3.2. Software Defined Radio (SDR)-basierte Long Term Evolution (LTE)- Messung................................. 29 3.3. Integer Carrier Frequency Offset (IFO)-Erkennung ............ 30 3.4. Primary Synchronization Signal (PSS)-Erkennung .............. 38 3.5. Fractional Carrier Frequency Offset (FFO)-Erkennung ........ 44 3.6. Secondary Synchronization Signal (SSS)-Erkennung ............ 49 3.7. Simulation der gesamten Implementierung .................... 57 4. Zusammenfassung und Ausblick ....................... 62 Literaturverzeichnis................................ 65

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Cellular network deployment scenarios refer to how cellular networks are implementedand deployed by network operators to provide wireless connectivity to end users.These scenarios can vary based on capacity requirements, type of geographical area, populationdensity, and specific use cases. Radio Access Networks of different generations,such as 4G and 5G, may also have different deployments. Network deployment scenarioscover many aspects, but two major components are Configuration settings and PerformanceMeasures which refer to the network nodes, hardware build-up and softwaresettings, and the end user behavior and connectivity experience in the area covered by thewireless network.In this master thesis, the aim is to understand how different area types - such as Rural,Suburban, and Urban – affect the cellular network deployment in such areas. A novelframework was developed to label each node (base station) with the area type it is associatedwith. The framework utilizes spatial analytics on the dataset provided by Ericsson forthe LTE nodes working with 4G technology in combination with open-source libraries anddatasets such as GeoPy and H3 Kontur population dataset respectively, to create area typelabels. The area types are labeled based on the calculated population density served byeach node and are considered true labels based on manual sanity checks performed. A supervisedmachine learning model was used to predict the nodes based on the CM and PMdata to understand the strength of the relationship between the features and true labels.This thesis also includes analysis and insights about characteristic deployment scenariosunder different area types. The main goal of this master thesis is to utilize machinelearning to uncover the characteristic features of a variety of node groups inherent in atelecom network, which, in the long run, contributes to better service operation and optimizationof existing cellular infrastructure. Nodes (base station) are labeled in the datato be able to distinguish their associated area-type. In addition to this clustering is performedto uncover the inherent characteristic behavior groups in the data and comparethem against the output from the classification model. Lastly, the investigation was doneon the potential impact of node placements such as indoor or outdoor, on the correspondingfeatures.In conclusion, the study’s results showed us that a correlation exists between deploymentscenarios and the different areas. There are a few prevalent common denominatorsbetween the node groups such as Pathloss and NR Cell Relations that drive the classificationmodel to a better classification metric, F1 score. Clustering of CM and PM data uncoversinherent patterns in different node groups under different area types and providesinformation about characteristic features of the groups such as CM data displaying twoconfiguration setting clusters, and PM data showing three different user behavior patterns.

With growing space market and entry of more private companies into the industry, there arecompanies and stakeholders who would like to have a high order of accuracy mission outputrequirements. These requirements vary from mission to mission. This simply means that if acompany wants an Earth observation mission, the main requirement to be fulfilled would be tohave the highest resolution of image possible. In order to achieve this, the satellite carrying the camera payload would be required to bepointed in the right direction with utmost accuracy. For a satellite to be pointed in the rightdirection, the noise generated by the sensors and actuators on-board, which determines theattitude of the satellite and helps in changing it, should be minimized. The aim of this thesisis to design a method which could help in determining the right components to be procuredso that the pointing requirements of the satellite are fulfilled. This objective is achieved bydesigning algorithms in python and MATLAB. The values generated by these algorithms, ultimately describe the type of sensor or actuator to be procured. Finally, the noise generated bysuch individual components act as pointing error source and then PEET is used to translatethese error sources to platform error, to generate a pointing budget and ensure that all pointingrequirements are satisfied.

En la actualidad, la necesidad por automatizar procesos en diferentes áreas ha conducido al uso de vehículos aéreos no tripulados para la realización de tareas de forma autónoma y ágil. En este trabajo se desarrolla un método para la navegación autónoma de un vehículo aéreo no tripulado en espacios interiores privados de servicio de geolocalización. Para dicho método se diseña un módulo electrónico capaz de procesar imágenes para detectar códigos QR con lo cual tener información para realizar un plan de vuelo autónomo. La implementación se logra gracias a una computadora embebida (Raspberry Pi 3) que es capaz de realizar las tareas de comunicación y procesamiento en tiempo real para la navegación. También se hace uso del controlador de vuelo Pixhawk, un LIDAR para el control de la altitud de vuelo. En cuanto a software se hace uso de las librerías OpenCV y Dronekit para el lenguaje de programación Python. En los resultados se muestran el desempeño del módulo y las configuraciones necesarias para el óptimo funcionamiento del método de navegación.

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REST står för “Representational State Transfer” och är en arkitekturstil som har blivit ett populärt val för implementation av serviceorienterade arkitekturer (Haupt, Leymann &amp; Vukojevic-Haupt, 2018). REST är en koordinerad uppsättning av arkitektoniska restriktioner som syftar på att minimera latens och nätverkskommunikation, samtidigt som oberoendet och skalbarheten av komponentimplementering maximeras (Fielding &amp; Taylor, 2002). REST är främst baserat på HTTP (Hypertext Transfer Protocol) och skapandet av ett REST api går att genomföra med hjälp av de flesta programspråken. Ett REST api kan dock utvecklas med hjälp av många olika programspråk och vilket programspråk som ska användas för att få den bästa responstiden finns det i dagsläget väldigt lite forskning om. Detta arbetet kommer att jämföra två olika programspråk för att se vilket programspråk som kan användas för att utveckla ett REST api med den bästa responstiden. De programspråk som kommer att användas är Python och PHP. Resultatet på experimenten är att REST api:et skapat med hjälp av PHP har den bästa responstiden jämfört ett liknande REST api skapat med hjälp av Python. En slutsats kan då dras att ett REST api:et skapat med hjälp av PHP har den bästa responstiden. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>