Nowadays a very important aspect of telecommunication is quality of services. 3G networks offer all of the customers’ best quality for each type of information including voice/video and data transmission. One of the vast discussion in this area is increasing the throughput and prevent the congestion in rush traffic hours in the network. Congestion occurs when transfer rate in the network is lower than requested rate by application. Congestion leads to cell loss and dropped cells should be retransferred to recover the data which is double job and affect the throughput and even can affect the quality of the services. Time sensitive information (voice/video) requires no data loss and they employ Forward Error Correction (FEC) codes to recover the data. The number of the FEC codes should be kept small to prevent the overload in the network. In this paper I will show that just simple FEC is not enough when network interworks with Asynchronous Transfer Mode (ATM). A powerful buffer management gives higher throughput and in that condition block loss rates reduces. In addition, effective utilization of IuB interfaces that link the Radio Network Controller (RNC) and Base Station (BS) has another effect on throughput. Different service categories are used to transform different type of information. In this paper we have also introduced all kind of information types offered by 3G networks and further analyzed the weakness of the existing transmission phase in 3G networks.

Repeater communication is one promising candidate solution in future cellular networks because of its ability to increase throughput, data rate and coverage. It is also considered as one candidate technology feature in 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) Advanced. Traditionally repeaters have been active continuously and perform blind forwarding without knowing the signal. However the repeater in LTE Advanced is likely to include some advanced functionalities such as frequency selectivity, gain controllability, multi antenna ability, advanced antenna processing, optimum power control algorithm, etc. In this thesis, on-frequency repeaters with frequency selectivity and gain controllability are analyzed and it is shown that the performance of repeater is highly dependent on the environment. It is necessary that the composite path gain (two-hop link) must be better than direct path gain (direct link) and the interference is attenuated in order to use the repeaters. The repeater directional donor antenna can be employed to further improve these two-hop links. And finally the benefit of advanced repeater functionalities is larger for uplink than downlink especially in heavy interference scenario however power limitation is often a bottleneck in uplink.

Network coding is a useful tool to increase the multicast capacity of networks. The traditional approach to network coding involving XOR operation has several limitations such as low robustness and can support only two users/packets at a time,per relay, in the mixing process to achieve optimal error performance. We propose the employment of product coding at the relay station instead of xor and investigate such a system where we use the relay to generate product codes by combining packets from different users.Our scheme uses relays to transmit only the redundancy of the product code instead of the whole product code.We seek to employ product coding can be able to support more than two users/packets per relay per slot,while maintaining a good error performance. Our scheme can accomodate as many users per relay as the costituent block code allows, thus reducing the number of relays required in the network. Product codes also offer increased robustness and flexibility as well as several other advantages, such as proper structure for burst error correction without extra interleaving. We compare the performance of such a scheme to the conventional xor scheme and see that our scheme not only reduces the number of relays required but gives improved error performance as well as. Another encouraging result is that our scheme starts to significantly outperform the conventional one by introducing a gain at the relay.

Multi-hop relay networks are a recent trend in WiMAX 802.16j networks. Many studies on the viability of relay stations have been done. It shows that RS are good cost-effective solution to the increasing demands on wireless broadband services. One problem that faces the 802.16j standard is its topology. It is a tree based multi-hop relay network, which is very vulnerable to single point breakage. This thesis proposes a new robust pairing technique in 802.16j network; combining a mesh topology with maximal ratio combining at the access link. Maximal ratio combining takes advantage of the broadcast nature of relay stations to obtain diversity gain. Mesh topology is a more robust topology without increasing delay or decreasing throughput. Maximal ratio combining provides higher throughput per burst, together a total throughput is increase 5% per frame is achieved.

The data traffic in wireless networks has grown tremendously over the past few decades and is ever-increasing. Moreover, there is an enormous demand for speed as well. Future wireless networks need to support three generic heterogeneous services: enhanced mobile broadband(eMBB), ultra-reliable low latency communication (URLLC) and massive machine type communication (mMTC). Massive MIMO has shown to be a promising technology to meet the demands and is now an integral part of 5G networks.  To get high data rates, ultra densification of the network by deploying more base stations in the same geographical area is considered. This led to an increase in inter-cell interference which limits the capacity of the network. To mitigate the inter-cell interference, distributed MIMO is advocated. Cell-free massive MIMO is a promising technology to improve the capacity of the network. It leverages all the benefits from ultra densification, massive MIMO, and distributed MIMO technologies and operates without cell boundaries.  In this thesis, we study random access, extreme multiplexing capabilities, and synchronization aspects of distributed massive MIMO. In Paper A studies the activity detection in grant-free random access for mMTC in cell-free massive MIMO network. An algorithm is proposed for activity detection based on maximum likelihood detection and the results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.  RadioWeaves technology is a new wireless infrastructure devised for indoor applications leveraging the benefits of massive MIMO and cell-free massive MIMO. In Paper B, we study the extreme multiplexing capabilities of RadioWeaves which can provide high data rates with very low power. We observe that the RadioWeaves deployment can spatially separate users much better than a conventional co-located deployment, which outweighs the losses caused by grating lobes and thus saves a lot on transmit power.  Paper C studies the synchronization aspect of distributed massive MIMO. We propose a novel, over-the-air synchronization protocol, which we call as BeamSync, to synchronize all the different multi-antenna transmit panels. We also show that beamforming the synchronization signal in the dominant direction of the channel between the panels is optimal and the synchronization performance is significantly better than traditional beamforming techniques. / Efterfrågan på data ökar ständigt och kravet på hastighet har ökat enormt. Framtida trådlösa nätverk behöver stödja tre generiska heterogena tjänster: enhanced mobile broadband (eMBB), ultra-reliable low latency communica-tion (URLLC) och massive machine type communication (mMTC). Massiv MIMO har visat sig vara en lovande teknik för att möta efterfrågan och är nu en integrerad del av 5G-nätverket. För att få höga datahastigheter övervägs extrem förtätning av nätverket genom att distribuera fler basstationer i samma geografiska område. Detta leder till en ökning av intercellinterferens men systemets kapacitet begränsas av intercellinterferensen. För att mildra intercellinterferensen förespråkas distribuerad MIMO. Cellfri massiv MIMO utnyttjar alla fördelar från ultraförtätning, massiv MIMO och distribuerad MIMO-teknik och fungerar utan cellgränser. I denna avhandling studerar vi random access, extrema multiplexerings möjligheter och synkroniseringsaspekter av distribuerad massiv MIMO. I Paper A studeras aktivitetsdetekteringen i grant-free random access för mMTC i cellfria massiv MIMO-nätverk. En algoritm föreslås för aktivitetsdetektering baserad på˚ maximum likelihood-metoden och resultaten visar att den makro-diversitetsvinst som tillhandahålls av den cellfria arkitekturen förbättrar aktivitetsdetekteringsprestandan jämfört med samlokaliserad arkitektur när täckningsområdet är stort. RadioWeaves-teknologi är en ny trädlös infrastruktur utformad för inomhusapplikationer som utnyttjar fördelarna med massiv MIMO och cellfri massiv MIMO. I Paper B studerar vi den extrema multiplexeringsförmågan hos RadioWeaves som kan ge höga datahastigheter med mycket låg effekt. Vi observerar att RadioWeaves-arkitekturen kan rumsligt separera användare mycket bättre än en konventionell samlokaliserad arkitektur, som uppväger förlusterna orsakade av gitterlober och därmed sparar mycket på sändningseffekten. Paper C studerar synkroniseringsaspekten av distribuerad massiv MIMO. Vi föreslår ett nytt, over-the-air synkroniseringsprotokoll, som vi kallar Beam-Sync, för att synkronisera alla olika sändningspaneler med flera antenner. Vi visar också˚ att strålformningen av synkroniseringssignalen i den dominerande riktningen av kanalen mellan panelerna är optimal och synkroniseringsprestandan är betydligt bättre än traditionella strålformningstekniker.

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Digital communications and computers are having a tremendous impact on the world today. In order to meet the increasing demand for digital communication services, engineers must design systems in a timely and cost-effective manner. The number of technologies available for providing a given service is growing daily, covering transmission media, devices, and software. The resulting design, analysis, and optimization of performance can be very demanding and difficult. Over the past decades, a large body of computer-aided engineering techniques have been developed to facilitate the design process of complex technological systems. These techniques rely on models of devices and systems, both analytic and simulation, to guide the analysis and design throughout the life cycle of a system. Computer-aided design, analysis, and simulation of communication systems constitute a new and important part of this process. This thesis studies different aspects of the simulation of communication systems by covering some basic ideas, approaches, and methodologies within the simulation context. Performance measurement of a digital communication is the main focus of this thesis. However, some popular visual indicators of signal quality, which are often generated in a simulation to provide a qualitative sense of the performance of a digital system, are also considered. Another purpose of this thesis is to serve as a model for developing simulations or template of other systems. In other words, students learning to simulate a system can use the work presented here as a starting point. / Master of Science

Connected and autonomous vehicles (CAVs) are an emerging trend in the transport sector and their impact on transportation, the economy, society and the environment will be tremendous. Much like the automobile shaped the way humans travelled, lived and worked during the 20th century, CAVs have yet again the potential to affect and reform all of these areas. Besides the imminent technological challenges on the robotic aspect of making CAVs become a market-ready reality, a plethora of ethical, social and legal questions will have to be addressed along the line. Knowledge of and interaction with the surrounding infrastructure and other actors in the system will be essential for CAVs in order to pave the way for progressive solutions to urgent sustainability and mobility issues in transportation. Road networks, i.e. the networks of roads and intersections, are the core infrastructure on which CAVs will operate. Thus, having detailed knowledge about them is key for CAVs in order to take the right decisions on both short-term actions that will affect individual traffic users in immediate situations and long-term actions that will affect entire transportation systems in the long run. Machine learning is nowadays a popular choice to extract and conglomerate knowledge from large amounts of data – and large amounts of data can be obtained about road networks. However, classical machine learning models are incapable of harnessing the graph-structured nature of road networks sufficiently. Graph neural networks (GNNs) are machine learning models of growing popularity that can explicitly leverage the complex topological structure of node dependencies in graphs, such as the ones observed in road networks. Road networks are sparse graphs that reside in a euclidean space, and therefore different to typical graphs studied in the literature. Also, crowd-sourced road network graphs often have incomplete attributes and are generally lacking the fine-grained level of detail in their encoded information that would be required for CAVs. Identifying the best representation of road network graphs and complementing their lacking detail with auxiliary data is therefore an important research direction. This thesis, therefore, addresses data-driven classification in road networks from two directions: A) the general approach of learning on spatial graphs of road network with GNNs, and B) complementing road network graphs with auxiliary data. Specifically, this thesis and the included papers address the exemplary task of road classifications and make the following contributions to the field: Paper A analyses how GNNs can be applied to road networks and how the networks are best represented. Different aggregator functions are compared on final classification performances. A novel aggregator and a neighbourhood sampling method are introduced, and the line graph transformation is identified as a suitable representation of road network graphs for GNNs. Paper B complements the road network graphs with mobility data from millions of GPS trajectories and introduces an equitemporal node spacing to create road segments of equal travel time. It further introduces remote sensing vision data as a potent complement to overcome shortcom-ings of the graph-based representation for road networks. Simple hand-crafted low-level vision features are used in this work. However, both the equitemporal node spacing and the simple vision features clearly exhibit improved classification performances. Finally, Paper C consolidates the complement of remote sensing data to the road network graphs. Through a general visual feature encoding of state-of-the-art pretrained vision back-bones that are carefully fine-tuned to the remote sensing domain, a further performance boost on the road classification task is achieved. / Vernetzte und autonome Fahrzeuge sind ein aktueller Trend in der Mobilitätsindustrie und ihr Einfluss auf Transportsysteme, die Wirtschaft, die Gesellschaft und die Umwelt wird enorm sein. Die Erwartungen an diese Roboterautos sind dabei extrem hoch. Sie sollen helfen, dringliche Mobilitätsprobleme unserer zunehmend globalisierten, urbanen Lebensweise zu überwinden und nachhaltige Lösungen ermöglichen. Das Wissen um und die Interaktion mit der Infrastruktur und anderen Verkehrsteilnehmern sind dabei ausschlaggebend, um mit vernetzten und autonomen Fahrzeuge progressive Mobilitätslösungen entwickeln zu können. Das Straßennetzwerk ist dabei die Kerninfrastruktur, auf der diese Fahrzeuge agieren werden. Maschinelles Lernen und künstliche Intelligenz sind heutzutage populäre Methoden, um Wissen und Entschlüsse aus riesigen Datenmengen zu ziehen - und über das Straßennetzwerk können riesige Datenmengen gesammelt werden. Klassische Modelle des maschinellen Lernens jedoch nutzen die Graphstrutktur, die den Straßennetzwerken zugrunde liegt, nicht hinreichend aus. Doch in den vergangenen Jahren haben graphbasierte neuronale Netze (GNNs) eine immense Beliebtheit erlangt. GNNs sind Modelle des maschinellen Lernens, die explizit komplexe Topologien und Abhängigkeiten von Knoten in Graphen ausnutzen. Diese Abschlussarbeit befasst sich mit der Anwendung von GNNs auf Straßennetzwerke. Es wird erläutert, welche Repräsentationen von Straßennetzwerke für GNNs vonnöten sind. Gleichzeitig werden die Unzulänglichkeiten der Graphstruktur von Straßennetzwerken dargestellt. Die enthaltenen Publikationen befassen sich damit, Zusatzinformation aus GPS-Sensoren und Satellitenbildern zu entnehmen, um diese Unzulänglichkeiten zu überwinden. Diese Abschlussarbeit umfasst drei Publikationen und in allen dreien wird eine Klassifizierung von Straßen mittels GNNs vorgenommen. Die Klassifizierung ordnet den Straßen verschiedene Kategorien zu, die ihre relative Bedeutsamkeit innerhalb des Straßennetzwerks darstellen. Publikation A analysiert die generelle Anwendung von GNNs auf Straßennetzwerke. Verschiedene Aggregatorfunktionen - d.h. Funktionen, die die Informationen von benachbarten Straßen zusammenfassen - werden im Hinblick auf das Klassifizierungsvermögen miteinander verglichen. Des Weiteren werden eine neue Aggregatorfunktion sowie eine neue Methode zur Nachbarschaftsbestimmung von Straßen im Straßennetzwerk vorgestellt. Außerdem, wird in dieser Publikation der Kantengraph des Straßennetzwerkes, der eine Umkehrung der Knoten und Kanten des ursprünglichen Graphen darstellt, als geeignete Repräsentation hervorgehoben. Publikation B komplettiert das Straßennetzwerk mit Mobilitätsdaten von mehreren Millionen GPS-Trajektorien. Eine äquitemporale Segmentierung des Straßennetzwerkes wird vorgeschlagen. Die Segmentierung erzeugt eine Aufteilung des Straßennetzwerkes, in der jedes Straßensegment dieselbe Reisezeit hat. Außerdem, wird aufgezeigt, wie Satellitenbilder in die Graphstruktur des Straßennetzwerks integriert werden können. Von den Bildern werden handgefertigte, niedrigdimensionale Merkmale extrahiert und dem Straßennetzwerksdaten beigefügt. Sowohl die äquitemporale Segmentierung als auch die Beifügung der Satellitenbilder führen zu einer deutlichen Verbesserung des Klassifizierungsvermögens der GNNs. Abschließend bekräftigt Publikation C, dass Satellitenbilder geeignete Hilfsdaten für Straßennetzwerke sind. Es werden vortrainierte mehrschichtige neuronale Netze genutzt, um hochdimensionale Kodierungen der visuellen Merkmale von Satellitenbildern zu erzeugen. Diese Kodierungen führen erneut zu einer deutlichen Verbesserung des Klassifizierungsvermögens gegenüber der Modelle aus Publikation B. Jedoch zeigen die Ergebnisse aus Publikation C auch, dass diese mehrschichtigen neuronalen Netze behutsam auf die visuellen Merkmale von Satellitenbildern angepasst werden müssen. Dies geschieht durch sogenanntes Transferlernen mithilfe von generellen Datenbanken von Satellitenbildern.

This thesis studies the physical layer (PHY layer) of the IEEE 802.11n wireless local area network (WLAN) standard. The possibility of integrating a receiver designed according to the standard with software-defined radios is investigated. The proposed design was implemented in MATLAB and tested using two softwaredefined radios. One of the radios transmitted IEEE 802.11n signals whilst the other one captured them and sent them to a computer for decoding. In this way, evaluation of the proposed receiver design was done. The tests resulted in successfully decoded WLAN packets, although errors occured regularly due to distortions in the air. The proposed MATLAB design can be developed further, with more features, for future tests and research.