Resource management in future mobile networks: from millimetre-wave backhauls to airborne access networks

The next generation of mobile networks will connect vast numbers of devices and support services with diverse requirements. Enabling technologies such as millimetre-wave (mm-wave) backhauling and network slicing allow for increased wireless capacities and logical partitioning of physical deployments, yet introduce a number of challenges. These include among others the precise and rapid allocation of network resources among applications, elucidating the interactions between new mobile networking technology and widely used protocols, and the agile control of mobile infrastructure, to provide users with reliable wireless connectivity in extreme scenarios. This thesis presents several original contributions that address these challenges. In particular, I will first describe the design and evaluation of an airtime allocation and scheduling mechanism devised specifically for mm-wave backhauls, explicitly addressing inter-flow fairness and capturing the unique characteristics of mm-wave communications. Simulation results will demonstrate 5x throughput gains and a 5-fold improvement in fairness over recent mm-wave scheduling solutions. Second, I will introduce a utility optimisation framework targeting virtually sliced mm-wave backhauls that are shared by a number of applications with distinct requirements. Based on this framework, I will present a deep learning solution that can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms. The proposed solution outperforms a baseline greedy approach by up to 62%, in terms of network utility, while running orders of magnitude faster. Third, the thesis investigates the behaviour of the Transport Control Protocol (TCP) in Long-Term Evolution (LTE) networks and discusses the implications of employing Radio Link Control (RLC) acknowledgements under different link qualities, on the performance of transport protocols. Fourth, I will introduce a reinforcement learning approach to optimising the performance of airborne cellular networks serving users in emergency settings, demonstrating rapid convergence (approx. 2.5 hours on a desktop machine) and a 5dB improvement of the median Signal-to-Noise-plus-Interference-Ratio (SINR) perceived by users, over a heuristic based benchmark solution. Finally, the thesis discusses promising future research directions that follow from the results obtained throughout this PhD project

Técnicas de pré-codificação para sistemas multicelulares coordenados

Doutoramento em TelecomunicaçõesCoordenação Multicélula é um tópico de investigação em rápido crescimento e uma solução promissora para controlar a interferência entre células em sistemas celulares, melhorando a equidade do sistema e aumentando a sua capacidade. Esta tecnologia já está em estudo no LTEAdvanced sob o conceito de coordenação multiponto (COMP). Existem várias abordagens sobre coordenação multicélula, dependendo da quantidade e do tipo de informação partilhada pelas estações base, através da rede de suporte (backhaul network), e do local onde essa informação é processada, i.e., numa unidade de processamento central ou de uma forma distribuída em cada estação base. Nesta tese, são propostas técnicas de pré-codificação e alocação de potência considerando várias estratégias: centralizada, todo o processamento é feito na unidade de processamento central; semidistribuída, neste caso apenas parte do processamento é executado na unidade de processamento central, nomeadamente a potência alocada a cada utilizador servido por cada estação base; e distribuída em que o processamento é feito localmente em cada estação base. Os esquemas propostos são projectados em duas fases: primeiro são propostas soluções de pré-codificação para mitigar ou eliminar a interferência entre células, de seguida o sistema é melhorado através do desenvolvimento de vários esquemas de alocação de potência. São propostas três esquemas de alocação de potência centralizada condicionada a cada estação base e com diferentes relações entre desempenho e complexidade. São também derivados esquemas de alocação distribuídos, assumindo que um sistema multicelular pode ser visto como a sobreposição de vários sistemas com uma única célula. Com base neste conceito foi definido uma taxa de erro média virtual para cada um desses sistemas de célula única que compõem o sistema multicelular, permitindo assim projectar esquemas de alocação de potência completamente distribuídos. Todos os esquemas propostos foram avaliados em cenários realistas, bastante próximos dos considerados no LTE. Os resultados mostram que os esquemas propostos são eficientes a remover a interferência entre células e que o desempenho das técnicas de alocação de potência propostas é claramente superior ao caso de não alocação de potência. O desempenho dos sistemas completamente distribuídos é inferior aos baseados num processamento centralizado, mas em contrapartida podem ser usados em sistemas em que a rede de suporte não permita a troca de grandes quantidades de informação.Multicell coordination is a promising solution for cellular wireless systems to mitigate inter-cell interference, improving system fairness and increasing capacity and thus is already under study in LTE-A under the coordinated multipoint (CoMP) concept. There are several coordinated transmission approaches depending on the amount of information shared by the transmitters through the backhaul network and where the processing takes place i.e. in a central processing unit or in a distributed way on each base station. In this thesis, we propose joint precoding and power allocation techniques considering different strategies: Full-centralized, where all the processing takes place at the central unit; Semi-distributed, in this case only some process related with power allocation is done at the central unit; and Fulldistributed, where all the processing is done locally at each base station. The methods are designed in two phases: first the inter-cell interference is removed by applying a set of centralized or distributed precoding vectors; then the system is further optimized by centralized or distributed power allocation schemes. Three centralized power allocation algorithms with per-BS power constraint and different complexity tradeoffs are proposed. Also distributed power allocation schemes are proposed by considering the multicell system as superposition of single cell systems, where we define the average virtual bit error rate (BER) of interference-free single cell system, allowing us to compute the power allocation coefficients in a distributed manner at each BS. All proposed schemes are evaluated in realistic scenarios considering LTE specifications. The numerical evaluations show that the proposed schemes are efficient in removing inter-cell interference and improve system performance comparing to equal power allocation. Furthermore, fulldistributed schemes can be used when the amounts of information to be exchanged over the backhaul is restricted, although system performance is slightly degraded from semi-distributed and full-centralized schemes, but the complexity is considerably lower. Besides that for high degrees of freedom distributed schemes show similar behaviour to centralized ones

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

NOMA Assisted Wireless Caching: Strategies and Performance Analysis

Conventional wireless caching assumes that content can be pushed to local caching infrastructure during off-peak hours in an error-free manner; however, this assumption is not applicable if local caches need to be frequently updated via wireless transmission. This paper investigates a new approach to wireless caching for the case when cache content has to be updated during on-peak hours. Two non-orthogonal multiple access (NOMA) assisted caching strategies are developed, namely the push-then-deliver strategy and the push-and-deliver strategy. In the push-then-deliver strategy, the NOMA principle is applied to push more content files to the content servers during a short time interval reserved for content pushing in on-peak hours and to provide more connectivity for content delivery, compared to the conventional orthogonal multiple access (OMA) strategy. The push-and-deliver strategy is motivated by the fact that some users' requests cannot be accommodated locally and the base station has to serve them directly. These events during the content delivery phase are exploited as opportunities for content pushing, which further facilitates the frequent update of the files cached at the content servers. It is also shown that this strategy can be straightforwardly extended to device-to-device caching, and various analytical results are developed to illustrate the superiority of the proposed caching strategies compared to OMA based schemes

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems