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Belesaca Mendieta, Juan Diego

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1994-02-12

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0000-0001-8609-0358

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57220835720

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Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones(DEET), Cuenca, Ecuador

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Ecuador

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Facultad de Ingeniería
La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

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Belesaca Mendieta

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Juan Diego

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2020 - 20233
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Now showing 1 - 3 of 3
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    Publication
    Topological Evaluation of Realistic Mobility Models for Spontaneous Wireless Networks Using Graph Theory Metrics
    (Association for Computing Machinery, Inc, 2023) Belesaca Mendieta, Juan Diego; Avilés Parra, Pablo Felipe; Vázquez Rodas, Andrés Marcelo; Astudillo Salinas, Darwin Fabián; Pinto Nieto, Josué David
    In recent years, the exponential growth of mobile devices connected to access networks has led to the emergence of connection architectures characterized by a high density of end devices. This, in turn, has posed significant challenges in access management. As a result, the scientific community is increasingly recognizing the crucial need to develop equitable and unbiased access control mechanisms. A fundamental starting point is to conduct a comprehensive analysis of these massive end-device architectures, treating them as high-density graphs of interconnected nodes. In this work, we generated massive topologies/architectures using synthetic models of human mobility that accurately reflect real-world human behavior. Subsequently, we evaluated and compared these topologies using six key metrics derived from graph theory. Additionally, we established connections between nodes within each topology based on the concept of spontaneous Wireless Mesh Networks. The outcomes of our analysis shed light on mobility models that demonstrated superior performance in specific metrics, while also proposing a methodology to effectively characterize these mobility models
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    Publication
    A Comprehensive Ceiling Analysis of the Physical Layer Performance of the 5G NR
    (Association for Computing Machinery, Inc, 2023) Belesaca Mendieta, Juan Diego; Vázquez Rodas, Andrés Marcelo; Vázquez Rodas, Andres Marcelo
    Modern mobile communication systems, such as Fifth-Generation (5G) technology and beyond 5G, need to exhibit increased capacity, high level of efficiency, improved performance, low end-To-end delay, support to massive number of connections, quality of service and experience, among other requirements. A suboptimal configuration and/or operation of any component of the 5G network can significantly degrade the overall system performance. The physical layer of the radio access network plays a crucial role in the performance of the 5G system. Within this layer, three of the main components that have a significant impact are the characteristics of the propagation channels in which they operate, the synchronization scheme, and the channel estimation accuracy. These components directly influence the system performance and effectiveness. Therefore, this paper presents a comprehensive ceiling analysis of the physical layer of the 5G implemented according to the 3GPP standard. The evaluation of the system encompasses different and standardized channel conditions, synchronization schemes, and channel estimation methods. Rigorous and extensive simulations were conducted using the Matlab 5G NR toolbox for the PDSCH (Physical Downlink Shared Channel). The nodes were configured to operate in both macro-urban and indoor environments. The Clustered Delay Line (CDL) and Tapped Delay Line (TDL) channel models are evaluated under ideal channel estimation and synchronization conditions in each case. Subsequently, more realistic and practical configurations were considered. The simulation results provide quantitative insights of the maximum achievable throughput under various channel environments, including line-of-sight and nonline-of-sight conditions. These results help identify the specific physical layer components that have a greater impact on the throughput of the system. By pinpointing these components, researchers can focus their efforts on developing techniques aimed at enhancing the efficiency of the future beyond 5G networks.
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    Publication
    Artificial neural network performance evaluation for a hybrid power domain orthogonal/non-orthogonal multiple access (OMA/NOMA) system
    (Association for Computing Machinery, Inc, 2020) Belesaca Mendieta, Juan Diego; Ávila Campos, Pablo Esteban; Vázquez Rodas, Andrés Marcelo
    Next-generation wireless technologies face considerable challenges in terms of providing the required latency and connectivity for new heterogeneous mobile networks. Driven by these problems, this study focuses on increasing user connectivity together with system throughput. For doing so, we propose and evaluate a hybrid machine learning-driven orthogonal/non-orthogonal multiple access (OMA/NOMA) system. In this work, we use an artificial neural network (ANN) to assign an OMA or NOMA access method to each user equipment (UE). As part of this research we also evaluate the accuracy and training time of the three most relevant learning algorithms of ANN (L-M, BFGS, and OSS). The main objective is to increase the sum-rate of the mobile network in the introduced beamforming and mmWave channel environment. Simulation results show up to a $20%$ sum-rate average performance increase of the system using the ANN management in contrast to a random non-ANN managed system. The Leveberg-Marquard (L-M) training algorithm is the best overall algorithm for this proposed application as presents the highest accuracy of around $77%$ despite 37 minutes of training and lower accuracy of $73%$ with approximately 28 seconds of training time.