Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning

Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 173-194)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022This works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Introduction -- Matrix exponential and deep learning neural network modeling of cellular handovers -- Survivability modeling in cellular networks -- Multi connectivity based handover enhancement and adaptive fractional packet duplication in 5G cellular networks -- Deepslice and Secure5G: a deep learning framework towards an efficient, reliable and secure network slicing in 5G networks -- Conclusion and future scop

Context-awareness for ubiquitous media service delivery in next generation networks

Les récentes avancées technologiques permettent désormais la fabrication de terminaux mobiles de plus en plus compacts et dotés de plusieurs interfaces réseaux. Le nouveau modèle de consommation de médias se résume par le concept "Anytime, Anywhere, Any Device" et impose donc de nouvelles exigences en termes de déploiement de services ubiquitaires. Cependant la conception et le developpement de réseaux ubiquitaires et convergents de nouvelles générations soulèvent un certain nombre de défis techniques. Les standards actuels ainsi que les solutions commerciales pourraient être affectés par le manque de considération du contexte utilisateur. Le ressenti de l'utilisateur concernant certains services multimédia tels que la VoIP et l'IPTV dépend fortement des capacités du terminal et des conditions du réseau d'accès. Cela incite les réseaux de nouvelles générations à fournir des services ubiquitaires adaptés à l'environnement de l'utilisateur optimisant par la même occasion ses resources. L'IP Multimedia Subsystem (IMS) est une architecture de nouvelle génération qui centralise l'accès aux services et permet la convergence des réseaux fixe/mobile. Néanmoins, l'évolution de l'IMS est nécessaire sur les points suivants :- l'introduction de la sensibilité au contexte utilisateur et de la PQoS (Perceived QoS) : L'architecture IMS ne prend pas en compte l'environnement de l'utilisateur, ses préférences et ne dispose pas d'un méchanisme de gestion de PQOS. Pour s'assurer de la qualité fournit à l'utilisateur final, des informations sur l'environnement de l'utilisateur ainsi que ses préférences doivent transiter en cœur de réseau afin d'y être analysés. Ce traitement aboutit au lancement du service qui sera adapté et optimisé aux conditions observées. De plus pour le service d'IPTV, les caractéristiques spatio-temporelles de la vidéo influent de manière importante sur la PQoS observée côté utilisateur. L'adaptation des services multimédias en fonction de l'évolution du contexte utilisateur et de la nature de la vidéo diffusée assure une qualité d'expérience à l'utilisateur et optimise par la même occasion l'utilisation des ressources en cœur de réseau.- une solution de mobilité efficace pour les services conversationnels tels que la VoIP : Les dernières publications 3GPP fournissent deux solutions de mobilité: le LTE proposeMIP comme solution de mobilité alors que l'IMS définit une mobilité basée sur le protocoleapplicatif SIP. Ces standards définissent le système de signalisation mais ne s'avancent pas sur la gestion du flux média lors du changement d'interface réseau. La deuxième section introduit une étude comparative détaillée des solutions de mobilité dans les NGNs.Notre première contribution est la spécification de l'architecture globale de notre plateforme IMS sensible au contexte utilisateur réalisée au sein du projet Européen ADAMANTIUM. Nous détaillons tout d'abord le serveur MCMS intelligent placé dans la couche application de l'IMS. Cet élément récolte les informations de qualité de services à différents équipements réseaux et prend la décision d'une action sur l'un de ces équipements. Ensuite nous définissons un profil utilisateur permettant de décrire son environnement et de le diffuser en coeur de réseau. Une étude sur la prédiction de satisfaction utilisateur en fonction des paramètres spatio-temporels de la vidéo a été réalisée afin de connaître le débit idéal pour une PQoS désirée.Notre deuxième contribution est l'introduction d'une solution de mobilité adaptée aux services conversationnels (VoIP) tenant compte du contexte utilisateur. Notre solution s'intègre à l'architecture IMS existante de façon transparente et permet de réduire le temps de latence du handover. Notre solution duplique les paquets de VoIP sur les deux interfaces actives pendant le temps de la transition. Parallèlement, un nouvel algorithme de gestion de mémoire tampon améliore la qualité d'expérience pour le service de VoIP.The latest advances in technology have already defied Moore s law. Thanks to research and industry, hand-held devices are composed of high processing embedded systems enabling the consumption of high quality services. Furthermore, recent trends in communication drive users to consume media Anytime, Anywhere on Any Device via multiple wired and wireless network interfaces. This creates new demands for ubiquitous and high quality service provision management. However, defining and developing the next generation of ubiquitous and converged networks raise a number of challenges. Currently, telecommunication standards do not consider context-awareness aspects for network management and service provisioning. The experience felt by the end-user consuming for instance Voice over IP (VoIP) or Internet Protocol TeleVision (IPTV) services varies depending mainly on user preferences, device context and network resources. It is commonly held that Next Generation Network (NGN) should deliver personalized and effective ubiquitous services to the end user s Mobile Node (MN) while optimizing the network resources at the network operator side. IP Multimedia Subsystem (IMS) is a standardized NGN framework that unifies service access and allows fixed/mobile network convergence. Nevertheless IMS technology still suffers from a number of confining factors that are addressed in this thesis; amongst them are two main issues :The lack of context-awareness and Perceived-QoS (PQoS):-The existing IMS infrastructure does not take into account the environment of the user ,his preferences , and does not provide any PQoS aware management mechanism within its service provisioning control system. In order to ensure that the service satisfies the consumer, this information need to be sent to the core network for analysis. In order to maximize the end-user satisfaction while optimizing network resources, the combination of a user-centric network management and adaptive services according to the user s environment and network conditions are considered. Moreover, video content dynamics are also considered as they significantly impact on the deduced perceptual quality of IPTV services. -The lack of efficient mobility mechanism for conversational services like VoIP :The latest releases of Third Generation Partnership Project (3GPP) provide two types of mobility solutions. Long-Term Evolution (LTE) uses Mobile IP (MIP) and IMS uses Session Initiation Protocol (SIP) mobility. These standards are focusing on signaling but none of them define how the media should be scheduled in multi-homed devices. The second section introduces a detailed study of existing mobility solutions in NGNs. Our first contribution is the specification of the global context-aware IMS architecture proposed within the European project ADAptative Management of mediA distributioN based on saTisfaction orIented User Modeling (ADAMANTIUM). We introduce the innovative Multimedia Content Management System (MCMS) located in the application layer of IMS. This server combines the collected monitoring information from different network equipments with the data of the user profile and takes adaptation actions if necessary. Then, we introduce the User Profile (UP) management within the User Equipment (UE) describing the end-user s context and facilitating the diffusion of the end-user environment towards the IMS core network. In order to optimize the network usage, a PQoS prediction mechanism gives the optimal video bit-rate according to the video content dynamics. Our second contribution in this thesis is an efficient mobility solution for VoIP service within IMS using and taking advantage of user context. Our solution uses packet duplication on both active interfaces during handover process. In order to leverage this mechanism, a new jitter buffer algorithm is proposed at MN side to improve the user s quality of experience. Furthermore, our mobility solution integrates easily to the existing IMS platform.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Concurrent Multipath Transfer: Scheduling, Modelling, and Congestion Window Management

Known as smartphones, multihomed devices like the iPhone and BlackBerry can simultaneously connect to Wi-Fi and 4G LTE networks. Unfortunately, due to the architectural constraints of standard transport layer protocols like the transmission control protocol (TCP), an Internet application (e.g., a file transfer) can use only one access network at a time. Due to recent developments, however, concurrent multipath transfer (CMT) using the stream control transmission protocol (SCTP) can enable multihomed devices to exploit additional network resources for transport layer communications. In this thesis we explore a variety of techniques aimed at CMT and multihomed devices, such as: packet scheduling, transport layer modelling, and resource management. Some of our accomplishments include, but are not limited to: enhanced performance of CMT under delay-based disparity, a tractable framework for modelling the throughput of CMT, a comparison of modelling techniques for SCTP, a new congestion window update policy for CMT, and efficient use of system resources through optimization. Since the demand for a better communications system is always on the horizon, it is our goal to further the research and inspire others to embrace CMT as a viable network architecture; in hopes that someday CMT will become a standard part of smartphone technology

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Advanced Technologies for Energy Saving, Wireless Backhaul and Mobility Management in Heterogeneous Networks

In recent years, due to the increasing number of existing and new devices and applications, the wireless industry has experienced an explosion of data traffic usage. As a result, new wireless technologies have been developed to address the capacity crunch. Long-Term Evolution-Licensed Assisted Access (LTE-LAA) is developed to provide the tremendous capacity by extending LTE to 5 GHz unlicensed spectrum. Hyper-dense small cells deployment is another promising technique that can provide a ten to one hundred times capacity gain by bringing small cells closer to mobile user equipments [1]. In this thesis, I focus on three problems related to these two techniques. In Chapter 3, I present a novel activation and sleep mechanism for energy efficient small cell heterogeneous networks (HetNets). In the cell-edge area of a macrocell, the coverage area of a sleeping small-cell will be covered by a range of expanded small-cells nearby. In contrast, in areas close to the macrocell, user equipment (UE) associated with a sleeping small cell will be distributed to the macrocell. Furthermore, the enhanced inter-cell interference coordination (eICIC) technique is used to support range-expanded small cells to avoid Quality of Service (QoS) degradation. Under both hexagonal and stochastic geometry based models, it is demonstrated that the proposed sleeping mechanism significantly reduces the energy consumption of the network compared with the conventional methods while guaranteeing the QoS requirements. Small cells are currently connected to limited backhaul to reduce the deployment and operational costs. In Chapter 4, an optimisation scheme is proposed for small cells to utilise the bandwidth of macrocells as wireless backhaul. I provide the numerical analysis of the performance of both the targeted small cell and the whole network. In Chapter 5, the mobility management (MM) of heterogeneous and LTE-LAA networks are investigated. To avoid Ping-Pong handover (PPHO) and reduce handover failure rate in HetNets, a self-optimisation algorithm is developed to change the handover parameters of a base station automagically. Furthermore, the MM of LTE-LAA networks is analysed. A new handover mechanism is proposed for LTE-LAA networks. Compared with the conventional LTE networks, LTE-LAA networks trigger the handover not only by using UE mobility, but also by the availability of the unlicensed band. A comprehensive analysis of the handover triggering event and handover procedure is presented. Simulation results show that by introducing handover triggered by available unlicensed band, the ratio of handover to unlicensed spectrum has a significant improvement. Therefore, a noticeable enhanced throughput of UEs is achievable by LTE-LAA networks

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods