Role of satellite communications in 5G ecosystem: perspectives and challenges

The next generation of mobile radio communication systems – so-called 5G – will provide some major changes to those generations to date. The ability to cope with huge increases in data traffic at reduced latencies and improved quality of user experience together with a major reduction in energy usage are big challenges. In addition, future systems will need to embody connections to billions of objects – the so-called Internet of Things (IoT) which raises new challenges.Visions of 5G are now available from regions across the world and research is ongoing towards new standards. The consensus is a flatter architecture that adds a dense network of small cells operating in the millimetre wave bands and which are adaptable and software controlled. But what is the place for satellites in such a vision? The chapter examines several potential roles for satellites in 5G including coverage extension, IoT, providing resilience, content caching and multi-cast, and the integrated architecture. Furthermore, the recent advances in satellite communications together with the challenges associated with the use of satellite in the integrated satellite-terrestrial architecture are also discussed

The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook

There is no doubt that the world is currently experiencing a global pandemic that is reshaping our daily lives as well as the way business activities are being conducted. With the emphasis on social distancing as an effective means of curbing the rapid spread of the infection, many individuals, institutions, and industries have had to rely on telecommunications as a means of ensuring service continuity in order to prevent complete shutdown of their operations. This has put enormous pressure on both fixed and mobile networks. Though fifth generation mobile networks (5G) is at its infancy in terms of deployment, it possesses a broad category of services including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC), that can help in tackling pandemic-related challenges. Therefore, in this paper, we identify the challenges facing existing networks due to the surge in traffic demand as a result of the COVID-19 pandemic and emphasize the role of 5G empowered by artificial intelligence in tackling these problems. In addition, we also provide a brief insight on the use of artificial intelligence driven 5G networks in predicting future pandemic outbreaks, and the development a pandemic-resilient society in case of future outbreaks

Joint access-backhaul mechanisms in 5G cell-less architectures

Older generations of wireless networks, such as 1G and 2G were deployed using leased line, copper or fibre line as backhaul. Later, in 3G and 4G, microwave wireless links have also worked as backhaul links while the backbone of the network was still wireline-based. However, due to multiple different use cases and deployment scenarios of 5G, solo wireline based backhaul network is not a cost-efficient option for the operators anymore. For cost-efficient and fast deployment, wireless backhaul options are very attractive. As drawbacks, wireless backhaul links have capacity and distance limitations. To take the advantages of both the solutions, i.e., wired and wireless, 5G transport networks are anticipated to be a heterogeneous, complex, and with stringent performance requirements. To address the aforementioned challenges, wireless backhaul options are providing more attractive solutions, and hence, technologies using the same resources (e.g., frequency channels) may be used by both access and backhaul networks. In this scenario, blurring the separation line between access and backhaul networks allows resource sharing and cooperation between both the networks and minimizes the network deployment and maintenance cost significantly. Therefore, in 5G, the access and backhaul networks cannot be seen as separate entities; rather, we seek to integrate them together to ensure the best use of resources. In this thesis, firstly, we investigate the challenges and potential technologies of 5G transport network. Later, to address these challenges, we identify and present different approaches to perform joint access-backhaul mechanism. An initial performance evaluation of access-aware backhaul optimization is presented, where backhaul network is dynamically assigned with the required resources to serve the dynamic requirements of a 5G access network. The evaluation results and discussions manifest the resource efficiency of joint access-backhaul mechanisms. Functional splits in different layers of the access network comes as an intelligent solution to reduce the enormous capacity requirements of the transport network in a centralized radio access network approach, which tends to centralize almost all the functionalities into a central unit, leaving only radio frequency functions at the access points. From the joint access-backhaul mechanism perspective, we propose a novel technique, which takes the benefit of functional splits at physical layer, to design a heterogeneous transport network in an economical budget-limited and capacity-limited scenario. Till today, the limited capacity of the wireless backhaul links remains a challenge, and hence, frequency spectrum becomes scarce, and requires efficient utilization. To address this challenge, a joint spectrum sharing technique to implement joint accessbackhaul mechanism is presented. Evaluation results show that our proposed joint spectrum sharing technique, where spectrum allocation in the backhaul network follows the access network's traffic load, is fair and efficient in terms of spectrum utilization. We also propose a machine learning technique, which analyses data from a real network and estimates access network's traffic pattern, and subsequently, assigns bandwidth in the access network according to the traffic estimations. Presented evaluation results show that a well-trained machine learning model can be very efficient to obtain an efficient utilization of frequency spectrum.Las primeras generaciones de redes móviles, se implementaron utilizando líneas de cobre o fibra para la conexión entre la red de acceso y el núcleo de la red (conexión backhaul). Más tarde, los enlaces inalámbricos también han funcionado como backhaul mientras que la columna vertebral de la red seguía basada en cable. Sin embargo, debido a los múltiples escenarios de implementación de 5G, una red de backhaul basada solamente en cable ya no es una opción rentable para los operadores. Para una implementación rentable y rápida, las opciones de backhaul inalámbrico son muy atractivas. Como inconvenientes, los enlaces backhaul inalámbricos tienen limitaciones de capacidad y distancia. Para aprovechar las ventajas de ambas soluciones, es decir, cableadas e inalámbricas, se prevé que las redes de transporte 5G sean heterogéneas, complejas y con estrictos requisitos de rendimiento. Para abordar los desafíos antes mencionados, las opciones de backhaul inalámbrico brindan soluciones más atractivas y, por lo tanto, las tecnologías que usan los mismos recursos (por ejemplo, canales de frecuencia) pueden usarse tanto en las redes de acceso como en las de backhaul. En este escenario, desdibujar la línea de separación entre las redes de acceso y backhaul permite el intercambio de recursos y la cooperación entre ambas redes, y minimiza significativamente los costes de implementación y mantenimiento de la red. Por lo tanto, en 5G las redes de acceso y backhaul no pueden verse como entidades separadas; más bien consideraremos su integración para asegurar el mejor uso de los recursos. En esta tesis, en primer lugar, investigamos los desafíos y las tecnologías potenciales para la implementación de la red de backhaul 5G. Más tarde, para abordar dichos desafíos, identificamos diferentes enfoques para un mecanismo conjunto de gestión de la red de acceso y backhaul. Se presenta una evaluación de rendimiento inicial para la optimización de backhaul que tiene en cuenta el estado de la red de acceso, donde la red de backhaul se equipa dinámicamente con los recursos necesarios para cumplir con los requisitos de la red de acceso 5G. Los resultados de la evaluación manifiestan la mayor eficiencia de los mecanismos de gestión de recursos que consideran redes de acceso y backhaul conjuntamente. Las divisiones funcionales en diferentes capas de la red de acceso (functional splits) se presentan como una solución inteligente para reducir los enormes requisitos de capacidad de la red de transporte en un enfoque de red de acceso, que tiende a centralizar casi todas las funcionalidades en una unidad central, dejando solo las funciones más relacionadas con la transmisión/recepción de señales en los puntos de acceso. Desde la perspectiva del mecanismo conjunto de red de acceso y backhaul, proponemos una técnica novedosa, que aprovecha las divisiones funcionales en la capa física para diseñar una red de transporte heterogénea con un presupuesto económico y un escenario de capacidad limitada. Hasta el día de hoy, la capacidad limitada de los enlaces inalámbricos sigue siendo un desafío, dado que el espectro de frecuencias es escaso y requiere una utilización eficiente. Para hacer frente a este desafío, se presenta una técnica de gestión de recursos espectrales compartidos entre red de acceso y backhaul. Los resultados de la evaluación muestran que nuestra propuesta, donde la asignación de espectro en la red de backhaul se hace de acuerdo a la carga de tráfico de la red de acceso, es justa y eficiente. También proponemos una técnica de aprendizaje automático, que analiza datos de una red real y estima el patrón de tráfico de la red de acceso para, posteriormente, asignar ancho de banda en la red de acceso de acuerdo con dichas estimaciones. Los resultados de la evaluación presentados muestran que un modelo de aprendizaje automático bien entrenado puede ser una herramienta muy útil a la hora de obtener una utilización eficiente del espectro de frecuencias.Postprint (published version

Models and Protocols for Resource Optimization in Wireless Mesh Networks

Wireless mesh networks are built on a mix of fixed and mobile nodes interconnected via wireless links to form a multihop ad hoc network. An emerging application area for wireless mesh networks is their evolution into a converged infrastructure used to share and extend, to mobile users, the wireless Internet connectivity of sparsely deployed fixed lines with heterogeneous capacity, ranging from ISP-owned broadband links to subscriber owned low-speed connections. In this thesis we address different key research issues for this networking scenario. First, we propose an analytical predictive tool, developing a queuing network model capable of predicting the network capacity and we use it in a load aware routing protocol in order to provide, to the end users, a quality of service based on the throughput. We then extend the queuing network model and introduce a multi-class queuing network model to predict analytically the average end-to-end packet delay of the traffic flows among the mobile end users and the Internet. The analytical models are validated against simulation. Second, we propose an address auto-configuration solution to extend the coverage of a wireless mesh network by interconnecting it to a mobile ad hoc network in a transparent way for the infrastructure network (i.e., the legacy Internet interconnected to the wireless mesh network). Third, we implement two real testbed prototypes of the proposed solutions as a proof-of-concept, both for the load aware routing protocol and the auto-configuration protocol. Finally we discuss the issues related to the adoption of ad hoc networking technologies to address the fragility of our communication infrastructure and to build the next generation of dependable, secure and rapidly deployable communications infrastructures