Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks

Hoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioeléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios. Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo tiempo. El algoritmo está formulado en términos de un problema de optimización multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-óptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras. El segundo algoritmo es un algoritmo de control de admisión conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologías al mismo tiempo. La principal diferencia con otros algorimos propuestos es que éstos últimos toman las dicisiones de admisión en cada tecnología por separado. Por ello, se necesita de algún mecanismo para seleccionar la tecnología a la que los usuarios se van a conectar. Por el contrario, la técnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogéneo. Por lo tanto, los usuarios no se enlazan con ninguna tecnología antes de ser admitidos.Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348Palanci

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities

This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark

Learning Augmented Optimization for Network Softwarization in 5G

The rapid uptake of mobile devices and applications are posing unprecedented traffic burdens on the existing networking infrastructures. In order to maximize both user experience and investment return, the networking and communications systems are evolving to the next gen- eration – 5G, which is expected to support more flexibility, agility, and intelligence towards provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and expanded with large sizes. Network softwarization is one of the critical enabling technologies to implement these requirements in 5G. In addition to these problems investigated in preliminary researches about this technology, many new emerging application requirements and advanced opti- mization & learning technologies are introducing more challenges & opportunities for its fully application in practical production environment. This motivates this thesis to develop a new learning augmented optimization technology, which merges both the advanced opti- mization and learning techniques to meet the distinct characteristics of the new application environment. To be more specific, the abstracts of the key contents in this thesis are listed as follows: • We first develop a stochastic solution to augment the optimization of the Network Function Virtualization (NFV) services in dynamical networks. In contrast to the dominant NFV solutions applied for the deterministic networking environments, the inherent network dynamics and uncertainties from 5G infrastructure are impeding the rollout of NFV in many emerging networking applications. Therefore, Chapter 3 investigates the issues of network utility degradation when implementing NFV in dynamical networks, and proposes a robust NFV solution with full respect to the underlying stochastic features. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. • Next, Chapter 4 aims to intertwin the traditional optimization and learning technologies. In order to reap the merits of both optimization and learning technologies but avoid their limitations, promissing integrative approaches are investigated to combine the traditional optimization theories with advanced learning methods. Subsequently, an online optimization process is designed to learn the system dynamics for the network slicing problem, another critical challenge for network softwarization. Specifically, we first present a two-stage slicing optimization model with time-averaged constraints and objective to safeguard the network slicing operations in time-varying networks. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. To address this, we combine the historical learning and Lyapunov stability theories, and develop a learning augmented online optimization approach. This facilitates the system to learn a safe slicing solution from both historical records and real-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, simulation experiments are also provided to demonstrate the considerable improvement of the proposals. • The success of traditional solutions to optimizing the stochastic systems often requires solving a base optimization program repeatedly until convergence. For each iteration, the base program exhibits the same model structure, but only differing in their input data. Such properties of the stochastic optimization systems encourage the work of Chapter 5, in which we apply the latest deep learning technologies to abstract the core structures of an optimization model and then use the learned deep learning model to directly generate the solutions to the equivalent optimization model. In this respect, an encoder-decoder based learning model is developed in Chapter 5 to improve the optimization of network slices. In order to facilitate the solving of the constrained combinatorial optimization program in a deep learning manner, we design a problem-specific decoding process by integrating program constraints and problem context information into the training process. The deep learning model, once trained, can be used to directly generate the solution to any specific problem instance. This avoids the extensive computation in traditional approaches, which re-solve the whole combinatorial optimization problem for every instance from the scratch. With the help of the REINFORCE gradient estimator, the obtained deep learning model in the experiments achieves significantly reduced computation time and optimality loss

5G RAN/MEC Slicing and Admission Control using Deep Reinforcement Learning

The 5G RAN functions can be virtualized and distributed across the radio unit (RU), distributed unit (DU), and centralized unit (CU) to facilitate flexible resource management. Complemented by multi-access edge computing (MEC), these components create network slices tailored for applications with diverse quality of service (QoS) requirements. However, as the requests for various slices arrive dynamically over time and the network resources are limited, it is non-trivial for an infrastructure provider (InP) to optimize its long-term revenue from real-time admission and embedding of slice requests. Prior works have leveraged Deep Reinforcement Learning (DRL) to address this problem, however, these solutions either do not scale to realistic topologies, require re-training of the DRL agents when facing topology changes, or do not consider the slice admission and embedding problems jointly. In this thesis, we use multi-agent DRL and Graph Attention Networks (GATs) to address these limitations. Specifically, we propose novel topology-independent admission and slicing agents that are scalable and generalizable to large and different metropolitan networks. Results show that the proposed approach converges faster and achieves up to 35.2% and 20% gain in revenue compared to heuristics and other DRL-based approaches, respectively. Additionally, we demonstrate that our approach is generalizable to scenarios and substrate networks previously unseen during training, as it maintains superior performance without re-training or re-tuning. Finally, we extract the attention maps of the GAT, and analyze them to detect potential bottlenecks and efficiently improve network performance and InP revenue through eliminating them

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters