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Der neue Mobilfunkstandard 5G bietet viele Verbesserungen im Vergleich zu seinem Vorgänger 4G. Während aus Sicht der Endverbraucher:innen die Erhöhung der Bandbreite am wichtigsten ist, ist der 5G Anwendungsfall Ultra-Reliable Low Latency Communications (URLLC) für das Internet der Dinge (IoT) am wichtigsten. Das IoT besteht aus vernetzten Geräten und Sensoren, die untereinander und mit zentralen Rechnern Daten austauschen. Häufig darf die Kommunikation zwischen den Sensoren und einem zentralen Rechner eine geringe Latenzzeit, z.B. 10 ms, nicht überschreiten. Während URLLC die Latenzzeit in 5G reduziert, kann die Verbindung zu einem weit entfernten zentralen Rechner erhebliche Latenzzeiten verursachen. Eine Lösung zur Behebung dieses Problems besteht darin, den zentralen Rechner physisch so nah wie möglich an das 5G-Endgerät zu bringen. Dies ist das Hauptkonzept von Edge Computing in 5G. Da ein mobiler Roboter als ein oder mehrere „Dinge“ im IoT eingestuft werden kann, besitzt er ähnliche Anforderungen. Wenn ein Roboter sich zum Beispiel in einer bekannten oder unbekannten Umgebung lokalisiert, stützt er sich auf Light Detection and Ranging (LIDAR) Scans, um die Welt zu erfassen. Dieses Verfahren wird als Simultaneous Localization and Mapping (SLAM) bezeichnet. Es ist von entscheidender Bedeutung, dass die Messungen des LIDAR-Sensors mit geringer Latenzzeit verarbeitet werden, da Verzögerungen die Genauigkeit der Lokalisierung beeinträchtigen können. SLAM ist jedoch auch sehr rechenintensiv, weshalb die Auslagerung dessen sehr vorteilhaft und manchmal auch notwendig ist, da Roboter oft an Hardwaregrenzen stoßen. Die zugrundeliegende Hypothese dieser Arbeit ist, dass es möglich ist, einen SLAM-Algorithmus an den Rand eines 5G-Netzwerks zu verlagern, ohne die Funktionalität und Qualität der Lokalisierung zu beeinträchtigen. Um dies zu erreichen, werden bestehende Ansätze von Edge Computing in 5G analysiert und verglichen. Darüber hinaus wird die quelloffene 5G Implementierung OpenAirInterface angepasst, um den bis dahin nicht berücksichtigen Anwendungsfall des Edge Computings zu unterstützen. Schließlich wird ein Experiment durchgeführt, das die Genauigkeit und Funktionalität des SLAM-Algorithmus in drei verschiedenen Einsatzgebieten vergleicht: Lokal, WiFi und 5G Edge. Die Ergebnisse dieser Arbeit zeigen, dass eine Berechnung von SLAM am 5G-Edge möglich ist, wobei die Genauigkeit und Funktionalität der Lokalisierung und der erstellten Karte im Vergleich zur WiFi-Option etwas geringer sind. Obwohl vieles noch optimiert werden kann, legt diese Arbeit den Grundstein für 5G-fähige mobile Robotik, vollständig mit Open-Source-Software betrieben., The novel mobile telecommunications standard 5G offers many improvements compared to its predecessor 4G. While from an end consumer perspective, the increase in bandwidth and download speed are the most crucial, the 5G use case Ultra-Reliable Low Latency Communications (URLLC) is the most important for the Internet of Things (IoT). The IoT consists of connected devices and sensors that exchange data with each other and central nodes. Often, the communication between the sensors and a central node must not exceed a low latency, e.g., 10ms. While URLLC reduces the latency in 5G, the link to a distant central node may introduce substantial latencies. A solution to solve this issue is to physically move the central node as close as possible to the connected 5G device. This is the main concept behind edge computing in 5G. As a mobile robot can be classified as one or more "things" in the IoT, it has similar requirements. For example, when a robot is localizing itself in a known or unknown environment, it relies on Light Detection and Ranging (LIDAR) scans to map the world. This procedure is called Simultaneous Localization and Mapping (SLAM). It is crucial that the measurements from the LIDAR sensor are processed with low latency, as delays can compromise the accuracy of the localization. However, Simultaneous Localization and Mapping (SLAM) is also computationally expensive. Therefore, it is highly desired and sometimes required to offload this computation to another computer, as robots are often hardware-constrained.The underlying hypothesis of this work is that it is possible to offload a SLAM algorithm to the edge of a 5G network without compromising functionality and quality of the localization. To achieve this, existing approaches to edge computing in 5G are analyzed and compared. Further, the open source 5G implementation OpenAirInterface is adapted to support the edge computing use case, which has not been considered in its initial design. Finally, an experiment is conducted which compares the accuracy and functionality of the SLAM algorithm in three different deployments: Local, WiFi and 5G edge. The results of this thesis show that offloading SLAM to the 5G edge is possible, whereas the accuracy and functionality of the localization and the produced map are slightly lower compared to the WiFi deployment. While much can still be optimized, this work lays the foundation for 5G-enabled mobile robotics, fully operated with open source software.

Recently, a growth of mobile networks, from a huge connection of only a few devices, to the need for constant maintenance with support even with different technological needs. This requires increasing the capacity of networks to respond to user needs, increasing connection speeds and decreasing latencies. In many cases, the demand for the capabilities offered by the new generation of mobile networks, 5G, remains unanswered with conventional structures, especially in urban areas. As a viable option for these needs, the use of small cells emerged. The use of this equipment is facilitated due to the flexibility offered by the architecture of 5G mobile networks that facilitate the division of the same into functional units with a virtual implementation, thus helping to spread the coverage area. The growing interest in 5G mobile networks and the immense possibilities they offer have given rise to projects focused on the development of 5G mobile networks that are made available for consultation and use by the interested community. These networks are mostly implemented in a virtual way, with the exception of the component responsible for the emission of the radio signal, where some options will be presented for this purpose during the course of the dissertation. As a target of study and evaluation of the state of development and usefulness throughout this dissertation, OpenAirInterface was chosen from among these open-source projects. The complete implementation of the same is presented and described, as well as the tests carried out in order to determine which are the functional bandwidths and which are the options for optimizing its operation. To conclude the work carried out, the results and balance of these tests are presented in the form of speed and latency tests in various bandwidths, verification of occupancy of the same, flexibility in modifying the emission frequency, as well as the result of a test of connecting elements of mobile networks developed by different open-source projects as a way of evaluating the flexibility of these networks. Recentemente temos assistido a um crescimento enorme de redes móveis, desde a conexão de apenas alguns dispositivos, até à necessidade de manter ligação constante com múltiplos equipamentos com necessidades tecnológicas diferentes. Isto requer o aumento da capacidade das redes para dar resposta às necessidades dos utilizadores, aumentar velocidades de conexão e diminuir latências. Em muitos casos, a procura pelas capacidades oferecidas pela nova geração de redes móveis, o 5G, continua sem resposta com as estruturas convencionais, especialmente em áreas urbanas. Como opção viável para essas necessidades, surgiu o uso de small cells. O uso desse equipamento é facilitado devido à flexibilidade oferecida pela arquitetura de redes móveis 5G que facilitam a divisão da mesma em unidades funcionais com uma implementação virtual ajudando assim à propagação da área de cobertura. O interesse crescente de redes móveis 5G e as imensas possibilidades que as mesmas oferecem, fizeram surgir projetos focados no desenvolvimento de redes móveis 5G que são colocados disponíveis para consulta e uso da comunidade interessada. Estes redes são maioritariamente implementadas de forma virtual à exceção do componente responsável pela emissão do sinal rádio, onde serão apresentadas algumas opções para o efeito no decorrer da dissertação. Como alvo de estudo e avaliação do estado de desenvolvimento e utilidade ao longo desta dissertação, foi escolhida a OpenAirInterface de entre esses projetos open-source. É apresentada e descrita a implementação completa da mesma, assim como os testes efetuados no sentido de apurar quais as larguras de banda funcionais e quais as opções de otimização de funcionamento da mesma. Para concluir o trabalho realizado, é apresentado o resultado e balanço desses testes na forma de testes de velocidade e latência em várias larguras de banda, verificação de ocupação da mesma, flexibilidade em modificar a frequência de emissão, assim como o resultado de um teste de conexão de elementos de redes móveis desenvolvidos por projetos open-source diferentes como forma de avaliar a flexibilidade destas redes. Mestrado em Engenharia Eletrónica e Telecomunicações

Cellular networks present high latencies,especially in the Radio Acccess Network (RAN). The new generation of cellular technology 5G, in addition to increasing transmission throughput, must offer low latency in mobile connectivity. The objective of this thesis is to propose mechanisms for reducing latency in the RAN. In this perspective, we set up a software-defined RAN experimentation platform based on the OpenAirInterface project and designed a tool for fine-grained measurement of RAN internal latencies called LatSeq. We show that the round-time trip latency typically reaches 30 ms in LTE divided between processing, queueing, retransmission and medium acquisition delays. We also measure a significant jitter on the uplink that affects the RAN latency but also the performance of TCP traffics,which are very common on the Internet. LatSeq highlights the role of the radio resource allocation mechanism in the jitter and latency of the uplink channel. We show how the partial knowledge of the transmission buffer length by the scheduling algorithm from Buffer Status Reports (BSRs) and Scheduling Requests (SRs) induces bursts of transmissions, significant jitter and under-utilization of the radio channel capacity. We then propose a new mechanism for estimating the terminal transmission buffer length which is compatible with 3GPP LTE and 5G standards. We demonstrate that this method makes it possible to reduce the latency of the uplink channel and even more the jitter experienced by packet flows; Les réseaux cellulaires présentent des latences importantes, en particulier au niveau du réseaux d’accès radio (RAN). La nouvelle génération de technologie cellulaire 5G, en plus d’augmenter les débits de transmission, doit offrir de faibles latences en connectivité mobile. L’objectif de cette thèse est de proposer des mécanismes de réduction de la latence dans le RAN. Dans cette perspective, nous avons mis en place une plateforme d’expérimentation software-defined RAN basée sur le projet OpenAirInterface et conçu un outil de mesure fine des latences internes au RAN appelé LatSeq. Nous montrons que la latence aller-retour atteint typiquement 30 ms en LTE répartie entre délais de traitement, de mis en file d’attente, de retransmissions et d’acquisition du medium. Nous avons aussi mesuré une gigue importante sur la voie remontante qui influe sur la latence du RAN mais aussi sur les performances des trafics TCP, très répandus sur Internet. LatSeq met en évidence le rôle du mécanisme d’allocation des ressources radio dans la gigue et la latence de la voie remontante. Nous montrons comment la connaissance imparfaite de la taille du buffer de transmission par l’algorithme d’ordonnancement à partir des rapports Buffer Status Reports (BSR) et des demandes d’accès (SR) induit une transmission par rafale, une gigue importante et une sous-utilisation de la capacité du canal radio. Nous proposons alors un nouveau mécanisme d’estimation de la taille du buffer de transmission des terminaux compatible avec les standards 3GPP LTE et 5G, et démontrons que cette méthode permet de réduire la latence de la voie remontante et la gigue subie par les flux de paquets.

[ES] La quinta generación de comunicaciones móviles cambia el paradigma de la arquitectura de red hacia el uso de funciones virtualizadas, no sólo a nivel de transporte (núcleo de red o core network), sino también a nivel de la red de acceso radio RAN (Radio Access Network), permitiendo el empleo de distintos softwares sobre un hardware genérico para facilitar las actualizaciones y la introducción de nuevas funcionalidades de manera ágil. Así mismo, existen nuevas corrientes que proponen la transcripción del concepto de código abierto a las interfaces y arquitecturas RAN, permitiendo el desarrollo de componentes a terceros y facilitando la interoperabilidad para ganar flexibilidad y eficiencia en los despliegues radio 5G. La apertura de los protocolos e interfaces de la RAN ha resultado en el concepto de una nueva arquitectura de red denominada Open RAN (O-RAN), que además busca introducir técnicas de IA/ML para el análisis de datos y la toma de decisiones a través de nuevos componentes como el RIC (Radio Intelligent Controller), ejecutándose on-premise, en el edge o en el cloud según las necesidades de latencia y requisitos del caso de uso concreto. En este trabajo se busca desplegar la RAN de una red 5G basada en el estándar O-RAN, empleando la implementación software de código abierto de OpenAirInterface (OAI) y dispositivos de radio definida por software USRPs, haciendo hincapié en el uso y analizando las posibilidades del novedoso controlador radio RIC introducido en la arquitectura O-RAN., [EN] The fifth generation of mobile communications changes the paradigm of network architecture towards the use of virtualized functions, not only at the transport level (core network), but also at the radio access network (RAN) level, allowing the use of multiple software over a generic hardware, to facilitate upgrades and the introduction of new functionalities in an agile manner. There are also new trends that propose the transcription of the open source concept to RAN interfaces and architectures, allowing the development of third-party components and facilitating interoperability to gain flexibility and efficiency in 5G radio deployments. The opening of RAN protocols and interfaces has resulted in the concept of a new network architecture called Open RAN (O-RAN), which also seeks to introduce AI/ML techniques for data analysis and decision making through new components such as the RIC (Radio Intelligent Controller), running on-premise, on the edge or in the cloud depending on latency needs and use case requirements. In this work we seek to deploy the RAN of a 5G network based on the O-RAN standard, employing the OpenAirInterface (OAI) open source software implementation and USRPs software defined radio devices, emphasizing the use and analyzing the possibilities of the novel RIC radio controller introduced in the O-RAN architecture.

With the continuous evolution of the mobile communications networks, it is important to have virtualized open-source ecosystems such as OpenAirInterface, that can provide low cost networks to the different operators. In this project we aim to develop a centralised radio controller for the management of 4G small cells based on OpenAirInterface technology. This radio controller interacts with the specific solution equivalent to OAI's RT RIC called FlexRAN. The objective of the radio controller is to achieve an automated deployment of 4G services over OAI technology cells, where each service is assigned several dedicated radio resources (RAN slice). Specifically, the aim is to manage the radio resources assigned to the different services dynamically and according to the requirements of the service (QoS, latency, etc.).