427 research outputs found

    Data Rescue : defining a comprehensive workflow that includes the roles and responsibilities of the research library.

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    Thesis (PhD (Research))--University of Pretoria, 2023.This study, comprising a case study at a selected South African research institute, focused on the creation of a workflow model for data rescue indicating the roles and responsibilities of the research library. Additional outcomes of the study include a series of recommendations addressing the troublesome findings that revealed data at risk to be a prevalent reality at the selected institute, showing the presence of a multitude of factors putting data at risk, disclosing the profusion of data rescue obstacles faced by researchers, and uncovering that data rescue at the institute is rarely implemented. The study consists of four main parts: (i) a literature review, (ii) content analysis of literature resulting in the creation of a data rescue workflow model, (iii) empirical data collection methods , and (iv) the adaptation and revision of the initial data rescue model to present a recommended version of the model. A literature review was conducted and addressed data at risk and data rescue terminology, factors putting data at risk, the nature, diversity and prevalence of data rescue projects, and the rationale for data rescue. The second part of the study entailed the application of content analysis to selected documented data rescue workflows, guidelines and models. Findings of the analysis led to the identification of crucial components of data rescue and brought about the creation of an initial Data Rescue Workflow Model. As a first draft of the model, it was crucial that the model be reviewed by institutional research experts during the next main stage of the study. The section containing the study methodology culminates in the implementation of four different empirical data collection methods. Data collected via a web-based questionnaire distributed to a sample of research group leaders (RGLs), one-on-one virtual interviews with a sample of the aforementioned RGLs, feedback supplied by RGLs after reviewing the initial Data Rescue Workflow Model, and a focus group session held with institutional research library experts resulted in findings producing insight into the institute’s data at risk and the state of data rescue. Feedback supplied by RGLs after examining the initial Data Rescue Workflow Model produced a list of concerns linked to the model and contained suggestions for changes to the model. RGL feedback was at times unrelated to the model or to data and necessitated the implementation of a mini focus group session involving institutional research library experts. The mini focus group session comprised discussions around requirements for a data rescue workflow model. The consolidation of RGL feedback and feedback supplied by research library experts enabled the creation of a recommended Data Rescue Workflow Model, with the model also indicating the various roles and responsibilities of the research library. The contribution of this research lies primarily in the increase in theoretical knowledge regarding data at risk and data rescue, and culminates in the presentation of a recommended Data Rescue Workflow Model. The model not only portrays crucial data rescue activities and outputs, but also indicates the roles and responsibilities of a sector that can enhance and influence the prevalence and execution of data rescue projects. In addition, participation in data rescue and an understanding of the activities and steps portrayed via the model can contribute towards an increase in the skills base of the library and information services sector and enhance collaboration projects with relevant research sectors. It is also anticipated that the study recommendations and exposure to the model may influence the viewing and handling of data by researchers and accompanying research procedures.Information SciencePhD (Research)Unrestricte

    Enabling High Throughput and Reliable Low Latency Communication over Vehicular Mobility in Next-Generation Cellular Networks

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    The fifth-generation (5G) networks and beyond need paradigm shifts to realize the exponentially increasing demands of next-generation services for high throughputs, low latencies, and reliable communication under various mobility scenarios. However, these promising features have critical gaps that need to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Although the sub-6 GHz bands can provide reliable and larger coverage, they cannot provide high data rates with low latencies due to a scarcity of spectrum available in these bands. Millimeter wave (mmWave) communication is a key enabler for a significant increase in the performance of these networks due to the availability of large bands of spectrum. However, the extremely limited transmission range of mmWave frequencies leads to poor reliability, especially for mobility scenarios. In this work, we present and evaluate the solutions in three key areas for achieving high throughput along with reliable low latency connection, especially for mobility scenarios in next-generation cellular networks. To enable the 5G networks to meet the demanding requirements of cellular networks, we look into (1) multi-connectivity for enhancing the performance of next-generation cellular networks, (2) designing a reliable network using multi-connectivity, and (3) developing a multilink scheme with efficient radio resource management. Despite the technological advances made in the design and evolution of 5G networks, emerging services impose stringent requirements which have not been fully met by 5G networks so far. The work in this dissertation aims to explore the challenges of future networks and address the needs in the three areas listed above. The results of the study open opportunities to resolve real-world 5G network issues. As 5G networks need to fulfill the rising performance demands of upcoming applications and industry verticals, we first study and evaluate multi-connectivity, which involves simultaneous connectivity with multiple radio access technologies or multiple bands, as a key enabler in improving the performance of the 5G networks. 5G networks are designed to have several small cells operating in the mmWave frequency range using ultra-dense networks (UDN) deployments to provide continuous coverage. But, such deployments not only face challenges in terms of frequent handovers, higher latency, lower reliability, and higher interference levels but also in terms of increasing complexity and cost of deployment, unbalanced load distributions, and power requirements. To address the challenges in high density base station deployments, we study and evaluate novel deployment strategies using multi-connectivity. In NR-NR Dual Connectivity (NR-DC), the user equipment (UE) is connected simultaneously to two gNBs, with one acting as the master node and the other as the secondary node to improve the performance of the 5G system. The master node operating at the sub-6 GHz bands provides high reliability, and the secondary node using the high bandwidth mmWave bands provides the high throughputs expected of 5G networks. This deployment also improves the latency as it decreases the number of handovers and link establishments. Thus, in this dissertation, we propose and evaluate novel 5G deployments with multi-connectivity, which can be used to ensure that these 5G networks are able to meet the demanding requirements of future services. The 5G networks also need to support ultra-reliable low latency communication, which refers to using the network for mission-critical communication that requires high reliability along with low latency. However, technological advancements so far have not been able to fully meet all these requirements. Thus, in this work, we design a reliable 5G network using multi-connectivity, which can simultaneously support high throughputs along with ultra-reliable low latency communication. Deployments using mmWave bands are highly susceptible to channel fluctuations and blockages. Thus, it is critical to consider new techniques and approaches that address these needs and can be implemented practically. In this work, we propose and implement a novel approach using packet duplication and its optimization in an NR-DC system to improve the performance of the system. In an NR-DC deployment with packet duplication, multiple instances of a packet are generated and transmitted simultaneously over different uncorrelated channels between the UE and the base stations, which decreases the packet failure probability. We also propose enhancements to the packet duplication feature for efficient radio resource utilization by looking into the distance of the UE from the base station, the velocity of the UE, and the received signal strength indicator (RSSI) levels. The proposed system improves the performance in terms of throughput, latency, and reliability under varying mobility scenarios. Finally, the 5G networks need to meet the increasing demands of uplink data traffic for applications such as autonomous driving, IoT applications, live video, etc. However, the uplink performance is lower compared to the downlink, and hence, it is critical for 5G to improve uplink performance. Thus, there are open research questions on what should be the network architecture with efficient radio resource utilization to meet the stringent requirements for mobility scenarios. In this work, we propose a novel uplink scheme where the UE performs only a single transmission on a common channel, and every base station that can receive this signal would accept and process it. This technique increases the probability of successful transmission and hence, increases the reliability of the network. It also removes the need to perform frequent handovers and allows high mobility with reduced latency. In this work, we propose and evaluate novel approaches for improving the performance of next-generation networks, which will be a key enabler for future applications. The proposed 5G techniques are shown to significantly improve the throughput, latency, and reliability simultaneously and are able to fulfill the stringent requirements of future services. Our work focuses on developing novel solutions for addressing the challenges involved in building next-generation cellular networks. In the future, we plan to further develop our system for real-world city-scale deployments

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Improved flat mobile core network architecture for 5G mobile communication systems

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    The current mobile network core is built based on a centralized architecture, including the S-GW and P-GW entities to serve as mobility anchors. Nevertheless, this architecture causes non-optimal routing and latency for control messages. In contrast, the fifth generation (5G) network will redesign the network service architecture to improve changeover management and deliver clients a better Quality-of-Experience (QoE). To enhance the design of the existing network, a distributed 5G core architecture is introduced in this study. The control and data planes are distinct, and the core network also combines IP functionality anchored in a multi-session gateway design. We also suggest a control node that will fully implement the control plane and result in a flat network design. Its architecture, therefore, improves data delivery, mobility, and attachment speed. The performance of the proposed architecture is validated by improved NS3 simulation to run several simulations, including attachment and inter- and intra-handover. According to experimental data, the suggested network is superior in terms of initial attachment, network delay, and changeover management

    Mission-Critical Communications from LMR to 5G: a Technology Assessment approach for Smart City scenarios

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    Radiocommunication networks are one of the main support tools of agencies that carry out actions in Public Protection & Disaster Relief (PPDR), and it is necessary to update these communications technologies from narrowband to broadband and integrated to information technologies to have an effective action before society. Understanding that this problem includes, besides the technical aspects, issues related to the social context to which these systems are inserted, this study aims to construct scenarios, using several sources of information, that helps the managers of the PPDR agencies in the technological decisionmaking process of the Digital Transformation of Mission-Critical Communication considering Smart City scenarios, guided by the methods and approaches of Technological Assessment (TA).As redes de radiocomunicações são uma das principais ferramentas de apoio dos órgãos que realizam ações de Proteção Pública e Socorro em desastres, sendo necessário atualizar essas tecnologias de comunicação de banda estreita para banda larga, e integra- las às tecnologias de informação, para se ter uma atuação efetiva perante a sociedade . Entendendo que esse problema inclui, além dos aspectos técnicos, questões relacionadas ao contexto social ao qual esses sistemas estão inseridos, este estudo tem por objetivo a construção de cenários, utilizando diversas fontes de informação que auxiliem os gestores destas agências na tomada de decisão tecnológica que envolve a transformação digital da Comunicação de Missão Crítica considerando cenários de Cidades Inteligentes, guiado pelos métodos e abordagens de Avaliação Tecnológica (TA)

    Seamless coverage for the next generation wireless communication networks

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    Data demand has exponentially increased due to the rapid growth of wireless and mobile devices traffic in recent years. With the advent of the fifth generation, 5G, and beyond networks, users will be able to take advantage of additional services beyond the capability of current wireless networks while maintaining a highquality experience. The exploitation of millimeter-wave (mm-wave) frequency in 5G promises to meet the demands of future networks with the motto of providing high data rate coverage with low latency to its users, which will allow future networks to function more efficiently. However, while planning a network using mm-wave frequencies, it is important to consider their small coverage footprints and weak penetration resistance. Heterogeneous network planning with the dense deployment of the small cells is one way of overcoming these issues, yet, without proper planning of the integrated network within the same or different frequencies could lead to other problems such as coverage gaps and frequent handovers; due to the natural physics of mm-wave frequencies. Therefore this thesis focuses on bringing ultra-reliable low-latency communication for mm-wave indoor users by increasing the indoor coverage and reducing the frequency of handovers. Towards achieving this thesis’s aim, a detailed literature review of mm-wave coverage is provided in Chapter 2. Moreover, a table that highlights the penetration loss of materials at various frequencies is provided as a result of thorough research in this field, which will be helpful to the researchers investigating this subject. According to our knowledge, this is the first table presenting the most studies that have been conducted in this field. Chapter 3 examines the interference effect of the outdoor base station (BS) inside the building in the context of a heterogeneous network environment. A single building model scenario is created, and the interference analysis is performed to observe the effects of different building materials used as walls. The results reveal the importance of choosing the material type when outdoor BS is close to the building. Moreover, the interference effect of outdoor BS should be minimized when the frequency re-use technique is deployed over very short distances. Chapter 4 presents two-fold contributions, in addition to providing a comprehensive handover study of mm-wave technology. The first study starts with addressing the problem of modelling users’ movement in the indoor environment. Therefore, a user-based indoor mobility prediction via Markov chain with an initial transition matrix is proposed, acquired from Q-learning algorithms. Based on the acquired knowledge of the user’s mobility in the indoor environment, the second contribution of this chapter provides a pre-emptive handover algorithm to provide seamless connection while the user moves within the heterogeneous network. The implementation and evaluation of the proposed algorithm show a reduction in the handover signalling costs by more than 50%, outperforming conventional handover algorithms. Lastly, Chapter 5 contributes to providing robust signal coverage for coverage blind areas and implementing and evaluating the proposed handover algorithm with the intelligent reflective surface. The results show a reduction in the handover signalling costs by more than 33%, outperforming conventional handover algorithms with the pre-emptive handover initiation

    Machine learning enabled millimeter wave cellular system and beyond

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    Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method

    2021-2022, University of Memphis bulletin

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    University of Memphis bulletin containing the graduate catalog for 2021-2022.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1441/thumbnail.jp

    2013-2014, University of Memphis bulletin

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    University of Memphis bulletin containing the graduate catalog for 2013-2014.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1434/thumbnail.jp
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