A flexible network architecture for 5G systems

In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the E

Characterisation of radio access network slicing scenarios with 5G QoS provisioning

5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning.Peer ReviewedPostprint (published version

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network

Contribution to the modelling and evaluation of radio network slicing solutions in 5G

Network slicing is a key feature of 5G architecture that allows the partitioning of the network into multiple logical networks, known as network slices, where each of them is customised according to the specific needs of a service or application. Thus, network slicing allows the materialisation of multi-tenant networks, in which a common network infrastructure is shared among multiple communication providers, acting as tenants and each of them using a different network slice. The support of multi-tenancy through slicing in the Radio Access Network (RAN), known as RAN slicing, is particularly challenging because it involves the configuration and operation of multiple and diverse RAN behaviours over the common pool of radio resources available at each of the RAN nodes. Moreover, this configuration needs to be performed in such a way that the specific requirements of each tenant are satisfied and, at the same time, the available radio resources are efficiently used. Therefore, new functionalities that allow the deployment of RAN slices are needed to be introduced at different levels, ranging from Radio Resource Management (RRM) functionalities that incorporate RAN slicing parameters to functionalities that support the lifecycle management of RAN slices. This thesis has addressed this need by proposing, developing and assessing diverse solutions for the support RAN slicing, which has allowed evaluating the capacities, requirements and limitations of network slicing in the RAN from diverse perspectives. Specifically, this thesis is firstly focused on the analytical assessment of RRM functionalities that support multi-tenant and multi-services scenarios, where services are defined according to their 5G QoS requirements. This assessment is conducted through the Markov modelling of admission control policies and the statistical modelling of the resourc allocation, both supporting multiple tenants and multiple services. Secondly, the thesis addresses the problem of slice admission control by proposing a methodology for the estimation of the radio resources required by a RAN slice based on data analytics. This methodology supports the decision on the admission or rejection of new RAN slice creation requests. Thirdly, the thesis explores the potential of artificial intelligence, and specifically, of Deep Reinforcement Learning (DRL) to deal with the capacity sharing problem in RAN slicing scenarios. To this end, a DRL-based capacity sharing solution that distributes the available capacity of a multi-cell scenario among multiple tenants is proposed and assessed. The solution consists in a Multi-Agent Reinforcement Learning (MARL) approach based on Deep Q-Network. Finally, this thesis discuses diverse implementation aspects of the DRL-based capacity sharing solution, including considerations on its compatibility with the standards, the impact of the training on the achieved performance, as well as the tools and technologies required for the deployment of the solution in the real network environment.El Network Slicing és una tecnologia clau de l’arquitectura del 5G que permet dividir la xarxa en múltiples xarxes lògiques, conegudes com a network slices, on cada una es configura d’acord a les necessitats d’un servei o aplicació específic. Així, el network slicing permet la materialització de les xarxes amb múltiples inquilins, on una infraestructura de xarxa comuna es comparteix entre diferents proveïdors de comunicacions, que actuen com a inquilins i utilitzen network slices diferents. El suport de múltiples inquilins mitjançant l’ús del network slicing a la xarxa d’accés ràdio (RAN), que es coneix com a RAN slicing, és un gran repte tecnològic, ja que comporta la configuració i operació de múltiples i diversos comportaments sobre els recursos ràdio disponibles a cadascun dels nodes de la xarxa d’accés. A més a més, aquesta configuració s’ha de portar a terme de forma que els requisits específics de cada inquilí es satisfacin i, al mateix temps, els recursos ràdio disponibles s’utilitzin eficientment. Per tant, és necessari introduir noves funcionalitats a diferents nivells que permetin el desplegament de les RAN slices, des de funcionalitats relacionades amb la gestió dels recursos ràdio (RRM) que incorporin paràmetres per al RAN slicing a funcionalitats que proporcionin suport a la gestió del cicle de vida de les RAN slices. Aquesta tesi ha adreçat aquesta necessitat proposant, desenvolupant i avaluant diverses solucions pel suport del RAN slicing, que han permès analitzar les capacitats, requisits i limitacions del RAN slicing des de diferents perspectives. Específicament, aquesta tesi es centra, en primer lloc, en realitzar una anàlisi de les funcionalitats de RRM que suporten escenaris amb múltiples inquilins i múltiples serveis, on els serveis es defineixen d’acord amb els seus requisits de 5G QoS. Aquesta anàlisi es porta a terme mitjançant la caracterització de polítiques de control d’admissió amb un model de Markov i el modelat estadístic de l’assignació de recursos, ambdós suportant múltiples inquilins i múltiples serveis. En segon lloc, la tesi aborda el problema del control d’admissió de network slices proposant una metodologia per l¿estimació dels recursos requerits per una RAN slice, que es basa en la anàlisi de dades. Aquesta metodologia dona suport a la decisió sobre l’admissió o rebuig de noves sol·licituds de creació de RAN slices. En tercer lloc, la tesi explora el potencial de la intel·ligència artificial, concretament, de les tècniques de Deep Reinforcement Learning (DRL) per a tractar el problema de la compartició de capacitat en escenaris amb RAN slicing. Amb aquest objectiu, es proposa i s’avalua una solució de compartició de capacitat basada en DRL que distribueix la capacitat disponible en un escenari multicel·lular entre múltiples inquilins. Aquesta solució es planteja com una solución de Multi-Agent Reinforcement Learning (MARL) basat en Deep Q-Network. Finalment, aquesta tesi tracta diversos aspectes relacionats amb la implementació de la solució de compartició de capacitat basada en DRL, incloent-hi consideracions sobre la compatibilitat de la solució amb els estàndards, l’impacte de l’entrenament de la solució al seu comportament i rendiment, així com les eines i tecnologies necessàries per al desplegament de la solució en un entorn de xarxa real.El Network Slicing es una tecnología clave de la arquitectura del 5G que permite dividir la red en múltiples redes lógicas, conocidas como network slices, que se configuran de acuerdo a las necesidades de servicios y aplicaciones específicas. Así, el network slicing permite la materialización de las redes con múltiples inquilinos, donde una infraestructura de red común se comparte entre diferentes proveedores de comunicaciones, que actúan como inquilinos y que usan network slices diferentes. El soporte de múltiples inquilinos mediante el uso del network slicing en la red de acceso radio (RAN), que se conoce como RAN slicing, es un gran reto tecnológico, ya que comporta la configuración y operación de múltiples y diversos comportamientos sobre los recursos radio disponibles en cada uno de los nodos de la red de acceso. Además, esta configuración debe realizarse de tal manera que los requisitos específicos de cada inquilino se satisfagan y, al mismo tiempo, los recursos radio disponibles se utilicen eficazmente. Por lo tanto, es necesario introducir nuevas funcionalidades a diferentes niveles que permitan el despliegue de las RAN slices, desde funcionalidades relacionadas con la gestión de recursos radio (RRM) que incorporen parámetros para el RAN slicing a funcionalidades que proporcionen soporte a la gestión del ciclo de vida de las RAN slices. Esta tesis ha abordado esta necesidad proponiendo, desarrollando y evaluando diversas soluciones para el soporte del RAN slicing, lo que ha permitido analizar las capacidades, requisitos y limitaciones del RAN slicing desde diversas perspectivas. Específicamente, esta tesis se centra, en primer lugar, en realizar un análisis de funcionalidades de RRM que soportan escenarios con múltiples inquilinos y múltiples servicios, donde los servicios se definen según sus requisitos de 5G QoS. Este análisis se lleva a cabo mediante la caracterización de políticas de control de admisión mediante un modelo de Markov y el modelado a nivel estadístico de la asignación de recursos, ambos soportando múltiples inquilinos y múltiples servicios. En segundo lugar, la tesis aborda el problema del control de admisión de network slices proponiendo una metodología para la estimación de los recursos radio requeridos por una RAN slice que se basa en análisis de datos. Esta metodología da soporte a la decisión sobre la admisión o el rechazo de nuevas solicitudes de creación de RAN slice. En tercer lugar, la tesis explora el potencial de la inteligencia artificial, y en concreto, de las técnicas de Deep Reinforcement Learning (DRL) para tratar el problema de compartición de capacidad en escenarios de RAN slicing. Para ello, se propone y evalúa una solución de compartición de capacidad basada en DRL que distribuye la capacidad disponible de un escenario multicelular entre múltiples inquilinos. Esta solución se plantea como una solución de Multi-Agent Reinforcement Learning (MARL) basado en Deep Q-Network. Finalmente, en esta tesis se tratan diversos aspectos relacionados con la implementación de la solución de reparto de capacidad basada en DRL, incluyendo consideraciones sobre su compatibilidad con los estándares, el impacto del entrenamiento en el comportamiento y rendimiento conseguido, así como las herramientas y tecnologías necesarias para su despliegue en un entorno de red real.Postprint (published version

Automotive Cognitive Access: Towards customized vehicular communication system

The evolution of Software Defined Networking (SDN) and Virtualization of mobile Network Functions (NFV) have enabled the new ways of managing mobile access systems and are seen as a major technological foundation of the Fifth Generation (5G) of mobile networks. With the appearance of 5G specifications, the mobile system architecture has the transition from a network of entities to a network of functions. This paradigm shift led to new possibilities and challenges. Existing mobile communication systems rely on closed and inflexible hardware-based architectures both at the access and core network. It implies significant challenges in implementing new techniques to maximize the network capacity, scalability and increasing performance for diverse data services. This work focuses preliminary on the architectural evolutions needed to solve challenges perceived for the next generation of mobile networks. I consider Software defined plus Virtualization featured Mobile Network (S+ MN) architecture as a baseline reference model, aiming at the further improvements to support the access requirements for diverse user groups. I consider an important class of things, vehicles, which needs efficient mobile internet access at both the system and application levels. I identify and describe key requirements of emerging vehicular communications and assess existing standards to determine their limitations. To provide optimized wireless communications for the specific user group, the 5G systems come up with network slicing as a potential solution to create customized networks. Network slicing has the capability to facilitates dynamic and efficient allocation of network resources and support diverse service scenarios and services. A network slice can be broadly defined as an end-to-end logically isolated network that includes end devices as well as access and core network functions. To this effect, I describe the enhanced behaviour of S+ MN architecture for the collection of network resources and details the potential functional grouping provided by S+ MN architecture that paves the way to support automotive slicing. The proposed enhancements support seamless connection mobility addressing the automotive access use case highly mobile environment. I follow the distribution of gateway functions to solve the problem of unnecessary long routes and delays. Exploiting the open SDN capabilities, the proposed S+ NC is able to parallelize the execution of certain control plane messages thus enabling the signalling optimisation. Furthermore, it enables the (Re)selection of efficient data plane paths with implied upper-layer service continuity mechanisms that remove the chains of IP address preservation for session continuity during IP anchor relocation. An implementation setup validates the proposed evolutions, including its core functionalities implemented using the ns-3 network simulator. The proposed slicing scheme has been evaluated through a number of scenarios such as numbers of signalling messages processed by control entities for an intersystem handover procedure relative to current mobile network architecture. I also perform the performance improvement analysis based on simulation results. Furthermore, I experimentally prove the feasibility of using Multipath TCP for connection mobility in intersystem handover scenario. The experiments run over the Linux Kernel implementation of Multipath TCP developed over the last years. I extend the Multipath TCP path management to delegates the management of the data paths according to the application needs. The implementation results have shown that the proposed S+ MN slicing architecture and enhancements achieve benefits in multiple areas, for example improving the mobility control and management, maintaining QoS, smooth handover, session continuity and efficient slice management and orchestration