Open Cell-less Network Architecture and Radio Resource Management for Future Wireless Communication Systems

In recent times, the immense growth of wireless traffic data generated from massive mobile devices, services, and applications results in an ever-increasing demand for huge bandwidth and very low latency, with the future networks going in the direction of achieving extreme system capacity and ultra reliable low latency communication (URLLC). Several consortia comprising major international mobile operators, infrastructure manufacturers, and academic institutions are working to develop and evolve the current generation of wireless communication systems, i.e., fifth generation (5G) towards a sixth generation (6G) to support improved data rates, reliability, and latency. Existing 5G networks are facing the latency challenges in a high-density and high-load scenario for an URLLC network which may coexist with enhanced mobile broadband (eMBB) services. At the same time, the evolution of mobile communications faces the important challenge of increased network power consumption. Thus, energy efficient solutions are expected to be deployed in the network in order to reduce power consumption while fulfilling user demands for various user densities. Moreover, the network architecture should be dynamic according to the new use cases and applications. Also, there are network migration challenges for the multi-architecture coexistence networks. Recently, the open radio access network (O-RAN) alliance was formed to evolve RANs with its core principles being intelligence and openness. It aims to drive the mobile industry towards an ecosystem of innovative, multi-vendor, interoperable, and autonomous RAN, with reduced cost, improved performance and greater agility. However, this is not standardized yet and still lacks interoperability. On the other hand, the cell-less radio access network (RAN) was introduced to boost the system performance required for the new services. However, the concept of cell-less RAN is still under consideration from the deployment point of view with the legacy cellular networks. The virtualization, centralization and cooperative communication which enables the cell-less RAN can further benefit from O-RAN based architecture. This thesis addresses the research challenges facing 5G and beyond networks towards 6G networks in regard to new architectures, spectral efficiency, latency, and energy efficiency. Different system models are stated according to the problem and several solution schemes are proposed and developed to overcome these challenges. This thesis contributes as follows. Firstly, the cell-less technology is proposed to be implemented through an Open RAN architecture, which could be supervised with the near real-time RAN intelligent controller (near-RT-RIC). The cooperation is enabled for intelligent and smart resource allocation for the entire RAN. Secondly, an efficient radio resource optimization mechanism is proposed for the cell-less architecture to improve the system capacity of the future 6G networks. Thirdly, an optimized and novel resource scheduling scheme is presented that reduces latency for the URLLC users in an efficient resource utilization manner to support scenarios with high user density. At the same time, this radio resource management (RRM) scheme, while minimizing the latency, also overcomes another important challenge of eMBB users, namely the throughput of those who coexist in such a highly loaded scenario with URLLC users. Fourthly, a novel energy-efficiency enhancement scheme, i.e., (3 × E) is designed to increase the transmission rate per energy unit, with stable performance within the cell-less RAN architecture. Our proposed (3 × E) scheme activates two-step sleep modes (i.e., certain phase and conditional phase) through the intelligent interference management for temporarily switching access points (APs) to sleep, optimizing the network energy efficiency (EE) in highly loaded scenarios, as well as in scenarios with lower load. Finally, a multi-architecture coexistence (MACO) network model is proposed to enable inter-connection of different architectures through coexistence and cooperation logical switches in order to enable smooth deployment of a cell-less architecture within the legacy networks. The research presented in this thesis therefore contributes new knowledge in the cellless RAN architecture domain of the future generation wireless networks and makes important contributions to this field by investigating different system models and proposing solutions to significant issues.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: Matilde Pilar Sánchez Fernández.- Secretario: Alberto Álvarez Polegre.- Vocal: José Francisco Monserrat del Rí

Improved planning and resource management in next generation green mobile communication networks

In upcoming years, mobile communication networks will experience a disruptive reinventing process through the deployment of post 5th Generation (5G) mobile networks. Profound impacts are expected on network planning processes, maintenance and operations, on mobile services, subscribers with major changes in their data consumption and generation behaviours, as well as on devices itself, with a myriad of different equipment communicating over such networks. Post 5G will be characterized by a profound transformation of several aspects: processes, technology, economic, social, but also environmental aspects, with energy efficiency and carbon neutrality playing an important role. It will represent a network of networks: where different types of access networks will coexist, an increasing diversity of devices of different nature, massive cloud computing utilization and subscribers with unprecedented data-consuming behaviours. All at greater throughput and quality of service, as unseen in previous generations. The present research work uses 5G new radio (NR) latest release as baseline for developing the research activities, with future networks post 5G NR in focus. Two approaches were followed: i) method re-engineering, to propose new mechanisms and overcome existing or predictably existing limitations and ii) concept design and innovation, to propose and present innovative methods or mechanisms to enhance and improve the design, planning, operation, maintenance and optimization of 5G networks. Four main research areas were addressed, focusing on optimization and enhancement of 5G NR future networks, the usage of edge virtualized functions, subscriber’s behavior towards the generation of data and a carbon sequestering model aiming to achieve carbon neutrality. Several contributions have been made and demonstrated, either through models of methodologies that will, on each of the research areas, provide significant improvements and enhancements from the planning phase to the operational phase, always focusing on optimizing resource management. All the contributions are retro compatible with 5G NR and can also be applied to what starts being foreseen as future mobile networks. From the subscriber’s perspective and the ultimate goal of providing the best quality of experience possible, still considering the mobile network operator’s (MNO) perspective, the different proposed or developed approaches resulted in optimization methods for the numerous problems identified throughout the work. Overall, all of such contributed individually but aggregately as a whole to improve and enhance globally future mobile networks. Therefore, an answer to the main question was provided: how to further optimize a next-generation network - developed with optimization in mind - making it even more efficient while, simultaneously, becoming neutral concerning carbon emissions. The developed model for MNOs which aimed to achieve carbon neutrality through CO2 sequestration together with the subscriber’s behaviour model - topics still not deeply focused nowadays – are two of the main contributions of this thesis and of utmost importance for post-5G networks.Nos próximos anos espera-se que as redes de comunicações móveis se reinventem para lá da 5ª Geração (5G), com impactos profundos ao nível da forma como são planeadas, mantidas e operacionalizadas, ao nível do comportamento dos subscritores de serviços móveis, e através de uma miríade de dispositivos a comunicar através das mesmas. Estas redes serão profundamente transformadoras em termos tecnológicos, económicos, sociais, mas também ambientais, sendo a eficiência energética e a neutralidade carbónica aspetos que sofrem uma profunda melhoria. Paradoxalmente, numa rede em que coexistirão diferentes tipos de redes de acesso, mais dispositivos, utilização massiva de sistema de computação em nuvem, e subscritores com comportamentos de consumo de serviços inéditos nas gerações anteriores. O trabalho desenvolvido utiliza como base a release mais recente das redes 5G NR (New Radio), sendo o principal focus as redes pós-5G. Foi adotada uma abordagem de "reengenharia de métodos” (com o objetivo de propor mecanismos para resolver limitações existentes ou previsíveis) e de “inovação e design de conceitos”, em que são apresentadas técnicas e metodologias inovadoras, com o principal objetivo de contribuir para um desenho e operação otimizadas desta geração de redes celulares. Quatro grandes áreas de investigação foram endereçadas, contribuindo individualmente para um todo: melhorias e otimização generalizada de redes pós-5G, a utilização de virtualização de funções de rede, a análise comportamental dos subscritores no respeitante à geração e consumo de tráfego e finalmente, um modelo de sequestro de carbono com o objetivo de compensar as emissões produzidas por esse tipo de redes que se prevê ser massiva, almejando atingir a neutralidade carbónica. Como resultado deste trabalho, foram feitas e demonstradas várias contribuições, através de modelos ou metodologias, representando em cada área de investigação melhorias e otimizações, que, todas contribuindo para o mesmo objetivo, tiveram em consideração a retro compatibilidade e aplicabilidade ao que se prevê que sejam as futuras redes pós 5G. Focando sempre na perspetiva do subscritor da melhor experiência possível, mas também no lado do operador de serviço móvel – que pretende otimizar as suas redes, reduzir custos e maximizar o nível de qualidade de serviço prestado - as diferentes abordagens que foram desenvolvidas ou propostas, tiveram como resultado a resolução ou otimização dos diferentes problemas identificados, contribuindo de forma agregada para a melhoria do sistema no seu todo, respondendo à questão principal de como otimizar ainda mais uma rede desenvolvida para ser extremamente eficiente, tornando-a, simultaneamente, neutra em termos de emissões de carbono. Das principais contribuições deste trabalho relevam-se precisamente o modelo de compensação das emissões de CO2, com vista à neutralidade carbónica e um modelo de análise comportamental dos subscritores, dois temas ainda pouco explorados e extremamente importantes em contexto de redes futuras pós-5G

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

Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN). The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage. First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes. While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs). DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements. Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN. Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization