Efficient offloading and load distribution based on D2D relaying and UAVs for emergent wireless networks

The device to device (D2D) and unmanned aerial vehicle (UAV) communications are considered as enabling technologies of the emergent 5th generation of wireless and cellular system (5G). Consequently, it is important to determine their corresponding performance with respect to the 5G requirements. In particular, we focus on enhancing the offloading and load balancing performance in three directions. In the first direction, we study the achievable data rate of user relay assisting other users in two-tier networks. We propose a novel heuristic communication scheme called device-for-device (D4D). The D4D enables moving users to share their resource by taking advantage of cooperative communication. We study the moving user rate sensitivity to the relay selection and blocking probability. In the second direction, we study the offloading from macrocell to small cell and load balancing among small cell. Also, we design a new utility weight function that enables a balanced relay assignment. We propose a novel low complexity algorithm for centralized scheme maximizing the load among small cells as well as users subject to SINR threshold constraints. The simulations show that our proposed schemes achieve performance in load balancing compared to those obtained with the previous or traditional method. In the third direction, we study the 3D deployment of multiple UAVs for emergent on-demand offloading. We propose a novel on-demand deployment scheme based on maximizing both the operator’s profit and the quality of service. The proposed scheme is based on solving a non-convex problem by combining k-means clustering with pattern search to find the suboptimal location of UAVs. The simulation results show that our proposed scheme maximizes the operator’s profit and improves offloading traffic efficiency. Our global contribution was the development of a scheme to improve the quality of service and the performance in emergent networks through the improvement of the load distribution and resource sharing using D2D and UAV

Self organisation for 4G/5G networks

Nowadays, the rapid growth of mobile communications is changing the world towards a fully connected society. Current 4G networks account for almost half of total mobile traffic, and in the forthcoming years, the overall mobile data traffic is expected to dramatically increase. To manage this increase in data traffic, operators adopt network topologies such as Heterogeneous Networks. Thus, operators can de­ ploy hundreds of small cells for each macro cell, allowing them to reduce coverage hales and/or lack of capacity. The advent of this technology is expected to tremendously increase the number of nodes in this new ecosystem, so that traditional network management activities based on, e.g., classic manual and field trial design approaches are just not be viable anymore. As a consequence, the academic J literature has dedicated a significant amount of effort to Self-Organising Network (SON) algorithms. These solutions aim to bring intelligence and autonomous adaptability into cellular networks, thereby reducing capital and operation expenditures (CAPEX/OPEX). Another aspect to take into account is that, these type of networks generate a large amount of data during their normal operation in the form of control, management and data measurements. This data is expected to increase in SG due to different aspects, such as densification, heterogeneity in layers and technologies, additional control and management complexity in Network Functions Virtualisation (NFV) and Software Defined Network (SDN), and the advent of the Internet of Things (loT), among others. In this context, operators face the challenge of de ­ signing efficient technologies, while introducing new services, reaching challenges in terms networks, which are self-aware, self-adaptive, and intelligent. This dissertation provides a contribution to the design, analysis, and evaluation of SON solutions to improve network opera tor performance, expenses, and users' experience, by making the network more self-adaptive and intelligent. It also provides a contribution to the design of a self-aware network planning tool, which allows to predict the Quality of Service (QoS) offered to end-users, based on data al ­ ready available in the network . The main thesis contributions are divided into two parts. The first part presents a novel functional architecture based on an automatic and self-organised Reinforcement Learning (RL) based approach to model SON functionalities, in which the main task is the self-coordination of different actions taken by different SON functions to be automatically executed in a self-organised realistic Long Term Evolution (LTE) network. The proposed approach introduces a new paradigm to deal with the conflicts genera ted by the concurrent execution of multiple SON functions, revealing that the proposed approach is general enough to modelali the SON functions and their derived conflicts. The second part of the thesis is dedicated to the problem of QoS prediction. In particular, we aim at finding patterns of knowledge from physical layer data acquired from heterogeneous LTE networks. We propose an approach that not only is able to verify the QoS level experienced by the users, through physical layer measurements of the UEs, but it is a lso able to predict it based on measurements collected at different time, and from different regions of the heterogeneous network. We propose then to make predictions independently of the physical location, in order to exploit the experience gained in other sectors of the network, to properly dimension and deploy heterogeneous nodes. In this context, we use Machine Learning (ML) as a tool to allow the network to learn from experience, improving performances, and big data analytics to drive the network from reactive to predictive.Hoy en día, el rápido crecimiento de las comunicaciones móviles está cambiando el mundo hacia una sociedad completamente conectada. Las redes 4G actuales representan casi la mitad del tráfico móvil total, y en los próximos años se espera que el tráfico total de los dispositivos móviles aumente drásticamente. Para gestionar este incremento de tráfico de datos, los operadores adoptan tecnologías de redes como las redes heterogéneas. De esta manera, los operadores pueden desplegar centena res de pequeñas celdas por cada macro celda, permitiendo reducir zonas sin cobertura y/o falta de capacidad. Con la introducción de esta tecnología, se espera que incremente de manera sustancia l el número de nodos en el nuevo ecosistema, de manera que las actividades de gestión de las redes tradicionales, basadas en, por ejemplo, el diseño manual, sean inviables. Como consecuencia, la literatura académica ha dedicado un esfuerzo significativo al diseño de algoritmos de redes auto-organizadas (SON). Estas soluciones tienen como objetivo introducir inteligencia y capacidad autónoma a las redes móviles, reduciendo la capacidad y costes operativos. Otro aspecto a tener en cuenta es que este tipo de redes generan una gran cantidad de datos durante su funcionamiento habitual, en forma de medidas de control y gestión de datos. Se espera que estos datos incrementen con la tecnología SG, debido a diferentes aspectos como los son la densificación de redes heterogéneas, la complejidad adicional en el control y la gestión de la virtualización de las funciones de redes (NFV) y las redes definidas por software (SON), así como la llegada del internet de las cosas (loT), entre otros. En este contexto, los operadores se enfrentan al reto de diseñar tecnologías eficientes, mientras introducen nuevos servicios, consiguiendo objetivos en términos de satisfacción del cliente, en donde el objetivo global del operador es la construcción de redes auto-conscientes, auto-adaptables e inteligentes. Esta tesis ofrece una contribución al diseño y evaluación de soluciones SON para mejorar el rendimiento de las redes, los costes y la experiencia de los usuarios, consiguiendo que la red sea auto-adaptable e inteligente. Así mismo, proporciona una contribución al diseño de una herramienta de planificación de red auto-consciente, que permita predecir la calidad de servicio brindada a los usuarios finales, basada en la explotación de datos disponibles en la red.Avui en dia, el ràpid creixement de les comunicacions mòbils està canviant el món cap a una societat completament connectada. Les xarxes 4G actuals representen casi la m trànsit mòbil total, i en els propers anys s’espera que el trànsit total de dades mòbils augmenti dràsticament. Per gestionar aquest increment de trànsit de dades, els operadors adopten topologies de xarxa com ara les xarxes heterogènies (HetNets). D’aquesta manera, els operadors poden desplegar centenars de cel·les petites per a cada cella macro, permetent reduir forats en la cobertura i/o la manca de capacitat. Amb l’arribada d’aquesta tecnologia, s’espera que incrementi enormement el nombre de nodes en el nou ecosistema, de manera que les activitats de gestió de xarxa tradicionals, basades en, per exemple, el disseny manual i els assaigs de camp esdevenen simplement inviables. Com a conseqüència, la literatura acadèmica ha dedicat una quantitat significativa d’esforç als algorismes de xarxa auto organitzada (SON). Aquestes solucions tenen com a objectiu portar la intel·ligència i capacitat d’adaptació autònoma a les xarxes mòbils, reduint el capital i les despeses operatives (CAPES/OPEX). Un altre aspecte a tenir en compte és que aquest tipus de xarxes generen una gran quantitat de dades durant el seu funcionament habitual, en forma de mesuraments de control, gestió i dades. S’espera que aquestes dades incrementin amb la tecnologia 5G, degut a diferents aspectes com ara la densificació, l’heterogeneïtat en capes i tecnologies, la complexitat addicional en el control i la gestió de la virtualització de les funcions de xarxa (NFV) i xarxes definides per software (SDN), i l’adveniment de la internet de les coses (IoT), entre d’altres. En aquest context, els operadors s’enfronten al repte de dissenyar tecnologies eficients, mentre introdueixen nous serveis, aconseguint objectius en termes de satisfacció del client, i on l’objectiu global d’un operador és la construcció de xarxes que són autoconscients, auto-adaptables i intel·ligents. Aquesta tesis ofereix una contribució al disseny, l’anàlisi i l’avaluació de les solucions SON per millorar el rendiment de l’operador de xarxa, les xi despeses i l’experiència dels usuaris, fent que la xarxa sigui més auto-adaptable i intel·ligent. També proporciona una contribució al disseny d’una eina de planificació de xarxa autoconscient, el que permet predir la qualitat de servei (QoS) oferta als usuaris finals, basada en dades ja disponibles a la xarxa. Les contribucions principals d’aquesta tesis es divideixen en dues parts. La primera part presenta una nova arquitectura funcional basada en un aprenentatge per reforç (RL) automàtic i auto-organitzat, enfocat en modelar funcionalitats SON, on la tasca principal és l’auto-coordinació de les diferents accions dutes a terme perles diferents funcions SON a ser executades de forma automàtica en una xarxa Long Term Evolution (LTE) auto-organitzada. L’enfocament proposat introdueix un nou paradigma perfer front als conflictes generats per l’execució simultània de múltiples funcions SON, revelant que l’enfocament proposat és prou general per modelar totes les funcions SON i els seus conflictes derivats. La segona part de la tesis està dedicada al problema de la predicció de la qualitat de servei. En particular, el nostre objectiu és trobar patrons de coneixement a partir de dades de la capa física adquirides de xarxes LTE heterogènies. Proposem un enfocament que no només és capaç de verificar el nivell de QoS experimentat pels usuaris, a través de mesuraments de la capa física dels UEs, sinó que també és capaç de predir-ho basant-se en mesuraments adquirits en diferents instants, i de diferents regions de la xarxa heterogènia. Proposem per tant fer prediccions amb independència de la ubicació física, aprofitant l’experiència adquirida en altres sectors de la xarxa, per dimensionar i desplegar nodes heterogenis correctament. En aquest context, utilitzem l’aprenentatge automàtic (ML) com a eina per permetre que la xarxa aprengui de l’experiència, millorant el rendiment, i l’anàlisi de grans volums de dades per a conduir la xarxa de reactiva a predictiva. Durant l’elaboració d’aquesta tesis, s’han extret dues conclusions principals clau. En primer lloc, destaquem la importància de dissenyar algorismes SON eficients per fer front eficaçment a diversos reptes, com ara la ubicació més adequada de funcions SON i algorismes per resoldre adequadament el problema d’implementació distribuïda o centralitzada, o la solució de conflictes entre funcions SON executades a diferents nodes o xarxes. En segon lloc, en termes d’eines de planificació de xarxes, es poden trobar diferents eines cobrint una àmplia gamma de sistemes i aplicacions orientades a la indústria, així com per a fins d’investigació. En aquest context, les solucions investigades són sotmeses contínuament a canvis importants, on un del principals impulsors és presentar solucions més rentable

Security for network services delivery of 5G enabled device-to-device communications mobile network

The increase in mobile traffic led to the development of Fifth Generation (5G) mobile network. 5G will provide Ultra Reliable Low Latency Communication (URLLC), Massive Machine Type Communication (mMTC), enhanced Mobile Broadband (eMBB). Device-to-Device (D2D) communications will be used as the underlaying technology to offload traffic from 5G Core Network (5GC) and push content closer to User Equipment (UE). It will be supported by a variety of Network Service (NS) such as Content-Centric Networking (CCN) that will provide access to other services and deliver content-based services. However, this raises new security and delivery challenges. Therefore, research was conducted to address the security issues in delivering NS in 5G enabled D2D communications network. To support D2D communications in 5G, this thesis introduces a Network Services Delivery (NSD) framework defining an integrated system model. It incorporates Cloud Radio Access Network (C-RAN) architecture, D2D communications, and CCN to support 5G’s objectives in Home Network (HN), roaming, and proximity scenarios. The research explores the security of 5G enabled D2D communications by conducting a comprehensive investigation on security threats. It analyses threats using Dolev Yao (DY) threat model and evaluates security requirements using a systematic approach based on X.805 security framework. Which aligns security requirements with network connectivity, service delivery, and sharing between entities. This analysis highlights the need for security mechanisms to provide security to NSD in an integrated system, to specify these security mechanisms, a security framework to address the security challenges at different levels of the system model is introduced. To align suitable security mechanisms, the research defines underlying security protocols to provide security at the network, service, and D2D levels. This research also explores 5G authentication protocols specified by the Third Generation Partnership Project (3GPP) for securing communication between UE and HN, checks the security guarantees of two 3GPP specified protocols, 5G-Authentication and Key Agreement (AKA) and 5G Extensive Authentication Protocol (EAP)-AKA’ that provide primary authentication at Network Access Security (NAC). The research addresses Service Level Security (SLS) by proposing Federated Identity Management (FIdM) model to integrate federated security in 5G, it also proposes three security protocols to provide secondary authentication and authorization of UE to Service Provider (SP). It also addresses D2D Service Security (DDS) by proposing two security protocols that secure the caching and sharing of services between two UEs in different D2D communications scenarios. All protocols in this research are verified for functional correctness and security guarantees using a formal method approach and semi-automated protocol verifier. The research conducts security properties and performance evaluation of the protocols for their effectiveness. It also presents how each proposed protocol provides an interface for an integrated, comprehensive security solution to secure communications for NSD in a 5G enabled D2D communications network. The main contributions of this research are the design and formal verification of security protocols. Performance evaluation is supplementary

An adaptive social-aware device-to-device communication mechanism for wireless networks

Device-to-Device (D2D) communication is an essential element in 5G networks, which enables users to communicate either directly without network assistance or with minimum signaling through a base station. For an effective D2D communication, related problems in mode and peer selection need to be addressed. In mode selection, the problem is how to guarantee selection always chooses the best available mode. In peer selection, the problem is how to select optimum peers among surrounding peers in terms of connection conditions and social relationships between peers. The main objectives of this research are to identify mode selection between devices and establishing a connection with the best D2D pair connection without privacy leakage. Multi-Attribute Decision Making and Social Choice theories are applied to achieve the objectives. Mode selection scheme is based on Received Signal Strength (RSS), delay and bandwidth attributes to choose and switch among the available modes intelligently based on the highest ranking. Then, the peering selection scheme is proposed using RSS, delay, bandwidth and power attribute to find an optimum peer with concerning social relationship, by evaluating trust level between peers and excluding the untrusted peers from ranking which will increase the optimum quality of D2D connection. The proposed schemes are validated and tested using MATLAB. Two main scenarios, namely crowded network and user speed were considered to evaluate the proposed mechanism with three existing approaches where the selection is based on a single attribute. The obtained results showed that the proposed mechanism outperforms other approaches in terms of delay, signal to noise ratio, delivery ratio and throughput with better performance up to 70%. The proposed mechanism provides a smooth switching between different modes and employs an automatic peering selection with trusted peers only. It can be applied in different types of network that serves the massive number of users with different movement speed of the user