This dissertation is directed towards improving the performance of 5G Wireless Fronthaul Networks and Wireless Sensor Networks, as measured by reliability, fault recovery time, energy consumption, efficiency, and security of transmissions, beyond what is achievable with conventional error control technology. To achieve these ambitious goals, the research is focused on novel applications of networking techniques, such as Diversity Coding, where a feedforward network design uses forward error control across spatially diverse paths to enable reliable wireless networking with minimal delay, in a wide variety of application scenarios. These applications include Cloud-Radio Access Networks (C-RANs), which is an emerging 5G wireless network architecture, where Remote Radio Heads (RRHs) are connected to the centralized Baseband Unit (BBU) via fronthaul networks, to enable near-instantaneous recovery from link/node failures. In addition, the ability of Diversity Coding to recover from multiple simultaneous link failures is demonstrated in many network scenarios. Furthermore, the ability of Diversity Coding to enable significantly simpler and thus lower-cost routing than other types of restoration techniques is demonstrated. Achieving high throughput for broadcasting/multicasting applications, with the required level of reliability is critical for the efficient operation of 5G wireless infrastructure networks. To improve the performance of C-RAN networks, a novel technology, Diversity and Network Coding (DC-NC), which synergistically combines Diversity Coding and Network Coding, is introduced. Application of DC-NC to several 5G fronthaul networks, enables these networks to provide high throughput and near-instant recovery in the presence of link and node failures. Also, the application of DC-NC coding to enhance the performance of downlink Joint Transmission-Coordinated Multi Point (JT-CoMP) in 5G wireless fronthaul C-RANs is demonstrated. In all these scenarios, it is shown that DC-NC coding can provide efficient transmission and reduce the resource consumption in the network by about one-third for broadcasting/multicasting applications, while simultaneously enabling near-instantaneous latency in recovery from multiple link/node failures in fronthaul networks. In addition, it is shown by applying the DC-NC coding, the number of redundant links that uses to provide the required level of reliability, which is an important metric to evaluate any protection system, is reduced by about 30%-40% when compared to that of Diversity Coding. With the additional goal of further reducing of the recovery time from multiple link/node failures and maximizing the network reliability, DC-NC coding is further improved to be able to tolerate multiple, simultaneous link failures with less computational complexity and lower energy consumption. This is accomplished by modifying Triangular Network Coding (TNC) and synergistically combining TNC with Diversity Coding to create enhanced DC-NC (eDC-NC), that is applied to Fog computing-based Radio Access Networks (F-RAN) and Wireless Sensor Networks (WSN). Furthermore, it is demonstrated that the redundancy percentage for protecting against n link failures is inversely related to the number of source data streams, which illustrates the scalability of eDC-NC coding. Solutions to enable synchronized broadcasting are proposed for different situations. The ability of eDC-NC coding scheme to provide efficient and secure broadcasting for 5G wireless F-RAN fronthaul networks is also demonstrated. The security of the broadcasting data streams can be obtained more efficiently than standardized methods such as Secure Multicasting using Secret (Shared) Key Cryptography.

This thesis establishes the need for a standardized, interoperable, front end interface to support the development of open RAN technologies, and establishes the viability and desirability of the VITA 49 interface standard as the alternative to other interface technologies. The purpose of this work is to propose a testbed platform for the further development for VITA 49 as a standard frontend interface as other current testbeds are not designed not as well suited to the VITA 49 standard or open RAN architecture. The VITA 49 interface standard provides a packetized interface between the front-end and the digital back-end of a split architecture system in a way that enables hardware interoperability between and within vendor supplies. The VITA 49 Radio Transport standard is ideally appropriate for integration into SDRs [12] due to its flexibility and metadata support. The REDHAWK platform is an integrated development environment which is used to develop a radio system that utilizes a remote radio unit to send and receive signals which transmits it using the VITA 49 protocol to the base band unit for processing. It was found that REDHAWK is better than GNURadio for this purpose, and that VRT technology is a much better than the current CPRI Standard as it provides an open standard, that enables a flexible, scalable interface that enables long-term growth.

La nouvelle génération de réseaux mobiles (5G) devrait faire face, durant les cinq prochaines années, à une importante croissance du volume de données, échangé entre plusieurs milliards d'objets et d'applications connectés. En outre, l'émergence de nouvelles technologies, telles que Internet of Things (IoT), conduite autonome et réalité augmentée, impose de plus fortes contraintes de performance et de qualité de service (QoS). Répondre aux besoins cités, tout en réduisant les dépenses d'investissement et d'exploitation (CAPEX/OPEX), sont les objectifs poursuivis par les opérateurs télécom, qui ont défini une nouvelle architecture d'accès radio, appelée Cloud Radio Access Network (C-RAN). Le principe du C-RAN est de centraliser, au sein d'un pool, les parties de traitement, BaseBand Unit (BBU), d'un RAN traditionnel. Les BBU sont alors dissociées de la station de base et de la partie radio, Remote Radio Unit (RRU). Ces deux parties restent néanmoins connectées à travers un réseau intermédiaire appelé Fronthaul (FH). Dans cette thèse, nous allons concevoir une nouvelle architecture C-RAN partiellement centralisée qui intègrera une plateforme de virtualisation basée sur un environnement Xen, nommée " Metamorphic Network " (MNet). A travers cette architecture, nous viserons à : i) mettre en place un pool, dans lequel des ressources physiques (processeurs, mémoire, ports réseaux, etc.) seront partagées entre des BBU virtualisées et d'autres applications, ii) établir un réseau FH ouvert aux fournisseurs de services et aux tierces parties, facilitant ainsi le déploiement des services au plus près des utilisateurs, pour une meilleure qualité d'expérience, iii) exploiter, à travers le FH, les infrastructures Ethernet existantes pour réduire les CAPEX/OPEX et enfin, iv) atteindre les performances réseau préconisées pour la 5G. Dans la première contribution, nous allons définir une nouvelle architecture Xen pour la plateforme MNet, intégrant le framework de packet processing, OpenDataPlane (ODP), au sein d’un domaine Xen privilégié, nommé « Driver Domain ». Notre objectif, à travers cette architecture, est d’accélérer le traitement des paquets de données transitant par MNet, en évitant la surutilisation, par ODP, des cœurs du processeur physique (CPU) de la plateforme. Pour cela, des cœurs CPU virtuels (vCPU) seront alloués dans le Driver Domain pour être exploités durant le traitement des paquets par ODP. Cette nouvelle plateforme MNet servira de base pour notre architecture C-RAN. Dans la seconde contribution, nous allons implémenter, au sein du FH, deux solutions réseau. La première solution, consistera à déployer le réseau de couche 2, Transparent Interconnection of Lots of Links (TRILL), pour connecter les différents éléments de notre architecture C-RAN. La seconde solution, consistera à déployer un réseau Software Defined Network (SDN), géré par le contrôleur distribué ONOS, qui sera virtualisé dans le pool BBU. Une comparaison des performances réseau sera réalisée entre ces deux solutions. / Over the next five years, the new generation of mobile networks (5G) would face a significant growth of the data volume, exchanged between billions of connected objects and applications. Furthermore, the emergence of new technologies, such as Internet of Things (IoT), autonomous driving and augmented reality, imposes higher performance and quality of service (QoS) requirements. Meeting these requirements, while reducing the Capital and Operation Expenditures (CAPEX/OPEX), are the pursued goals of the mobile operators. Consequently, Telcos define a new radio access architecture, called Cloud Radio Access Network (C-RAN). The C-RAN principle is to centralize, within a pool, the processing unit of a radio interface, named BaseBand Unit (BBU). These two units are interconnected through a Fronthaul (FH) network. In this thesis, we design a new partially centralized C-RAN architecture that integrates a virtualization platform, based on a Xen environment, called Metamorphic Network (MNet). Through this architecture, we aim to: i) implement a pool in which physical resources (processors, memory, network ports, etc.) are shared between virtualized BBUs and other applications; ii) establish an open FH network that can be used by multiple operators, service providers and third parties to deploy their services and Apps closer to the users for a better Quality of Experience (QoE); iii) exploit, through the FH, the existing Ethernet infrastructures to reduce CAPEX/OPEX; and finally iv) provide the recommended network performance for the 5G. In the first contribution, we define a new Xen architecture for the MNet platform integrating the packet-processing framework, OpenDataPlane (ODP), within a privileged Xen domain, called Driver Domain (DD). This new architecture accelerates the data packet processing within MNet, while avoiding the physical CPUs overuse by ODP. Thus, virtual CPU cores (vCPU) are allocated within DD and are used by ODP to accelerate the packet processing. This new Xen architecture improves the MNet platform by 15%. In the second contribution, we implement two network solutions within the FH. The first solution consist of deploying a layer 2 network protocol, Transparent Interconnection of Lots of Links (TRILL), to connect multiple elements of our C-RAN architecture. The second solution consists of implementing a Software Defined Network (SDN) model managed by Open Network Operating System (ONOS), a distributed SDN controller that is which is virtualized within BBU pool. Moreover, a network performance comparison is performed between these two solutions.

The growing adoption of millimeter wave (mmWave) turns efficient beamforming and beam management procedures into key enablers for 5th Generation (5G) and Beyond 5G (B5G) mobile networks. Recent research has sought to optimize beam management in modern Radio Access Network (RAN) architectures, where open, virtualized, disaggregated and multi-vendor environments are considered, and management platforms allow the use of Artificial Intelligence (AI) and Machine Learning (ML)-based solutions. Moreover, beam management represents some fundamental use cases defined by Open RAN Alliance (O-RAN). This work analyses beam management strategies in Open RAN and proposes solutions for codebook-based mmWave systems inspired by two use cases from O-RAN: the Grid of Beams (GoB) Optimization and the AI/ML-assisted Beam Selection. For the GoB Optimization use case, a scenario subject to constraints on the use of the full GoB due to overhead during beam selection is considered. An Advantage Actor Critic (A2C) learning-based framework is proposed to optimize the GoB, as well as the transmission power in a mmWave network. The proposed technique improves Energy Efficiency (EE) and ensures fair coverage is maintained. The simulations show that A2C-based joint optimization of GoB and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power, or the optimization of GoB and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test, and it is closer to the maximum theoretical EE. In the case of the AI/ML-assisted Beam Selection use case, the overhead during beam selection is addressed by a multi-modal sensing-aided ML-based method. When using sensing information sources external to the RAN in a multi-vendor disaggregated environment, such methods must account for privacy and data ownership issues. A Distributed Machine Learning (DML) strategy based on Split Learning (SL) is proposed to this end. The solution can cope with deployment challenges in novel RAN architectures and is applied to single and multi-level beam selection decisions, where the latter considers hierarchical codebook structures. With the proposed approach, accuracy levels above 90% can be achieved, while overhead decreases by 85% or more. SL achieves performance comparable to the centralized learning-based strategies, with the added value of accounting for privacy and data ownership issues.

No description available.

Yes / The delivery of anticancer agents to their subcellular sites of action is a significant challenge for effective cancer therapy. Peptides, which are integral to several oncogenic pathways, have significant potential to be utilised as cancer therapeutics due to their selectivity, high potency and lack of normal cell toxicity. Novel Ras protein-Regulator of chromosome condensation 1 (Ran-RCC1) inhibitory peptides designed to interact with Ran, a novel therapeutic target in breast cancer, were delivered by entrapment into polyethylene glycol-poly (lactic-co-glycolic acid) PEG-PLGA polymeric nanoparticles (NPs). A modified double emulsion solvent evaporation technique was used to optimise the physicochemical properties of these peptide-loaded biodegradable NPs. The anti-cancer activity of peptide-loaded NPs was studied in vitro using Ran-expressing metastatic breast (MDA-MB-231) and lung cancer (A549) cell lines, and in vivo using Solid Ehrlich Carcinoma-bearing mice. The anti-metastatic activity of peptide-loaded NPs was investigated using migration, invasion and colony formation assays in vitro. A PEG-PLGA-nanoparticle encapsulating N-terminal peptide showed a pronounced antitumor and anti-metastatic action in lung and breast cancer cells in vitro and caused a significant reduction of tumor volume and associated tumor growth inhibition of breast cancer model in vivo. These findings suggest that the novel inhibitory peptides encapsulated into PEGylated PLGA NPs are delivered effectively to interact and deactivate Ran. This novel Ran-targeting peptide construct shows significant potential for therapy of breast cancer and other cancers mediated by Ran overexpression.

The Rapid Automatized Naming test (RAN) has been shown to be a strong predictor of reading ability (Katzir et al., 2006), however the nature of this relationship remains unclear. The purpose of this study was to evaluate the underlying components of RAN, and to then determine whether these components partially account for the relationship between RAN and reading ability. The sample consisted of 100 undergraduate students. The underlying components of RAN that were evaluated included, visual search and scanning, auditory and visual sequencing, discrete naming, confrontation naming, executive functioning and phonological processing. The findings suggest that visual search and scanning, auditory sequential processing, discrete naming and executive functioning are all significant underlying components of RAN. Additionally, the findings suggest that visual scanning and auditory sequential processing partially mediate the relationship between RAN and reading fluency.

While significant research has been done on dyslexia with native English speakers, little is known about identifying dyslexia in English Language Learners (ELLs). Previous research, primarily involving monolingual native English speakers, has highlighted specific cognitive correlates of reading that help screen students with dyslexia at an early age. These cognitive correlates, which include Phonological Awareness (PA), Rapid Automatized Naming (RAN), and Processing Speed (PS), have consistently been linked to basic reading skills in English and other languages. The present study had many objectives that could provide guidance for practitioners working with ELLs. First it explored the value of PA, RAN, and PS as predictors of basic reading skills in Spanish speaking ELLs. Second, it investigated a model that combined tasks of PA, RAN and PS that had the highest correlations to aspects of reading. Third, it explored the role of Vocabulary (V) in both English and Spanish in the development of reading skills in ELLs. Fourth, it aimed at establishing the use of the Simple View of Reading (SVR; Gough & Tunmer, 1986), which postulates that reading is the product of decoding and listening comprehension skills, for the assessment of reading in ELLs. Finally, it incorporated findings from this study for an adaptation of the SVR for use with ELLs. Findings suggest that RAN, PA, and PS, as measured in this study, are highly correlated to basic reading skills in Spanish speaking ELLs, and that these can be used for the assessment of reading and dyslexia in this population. This study also demonstrated that a model that included RAN numbers and phoneme deletion in English can accurately predict reading performance. Findings also indicate that V in English is highly correlated to basic reading skills in English, and that the model of the SVR can be replicated with the population sampled in this study. Finally, case study analyses illustrate how a model similar to the SVR, which incorporates RAN and PS, as well as V, can be used for the assessment of reading and dyslexia in ELLs.

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