6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Development of a machine learning based methodology for bridge health monitoring

Tesi en modalitat de compendi de publicacionsIn recent years the scientific community has been developing new techniques in structural health monitoring (SHM) to identify the damages in civil structures specially in bridges. The bridge health monitoring (BHM) systems serve to reduce overall life-cycle maintenance costs for bridges, as their main objective is to prevent catastrophic failures and damages. In the BHM using dynamic data, there are several problems related to the post-processing of the vibration signals such as: (i) when the modal-based dynamic features like natural frequencies, modes shape and damping are used, they present a limitation in relation to damage location, since they are based on a global response of the structure; (ii) presence of noise in the measurement of vibration responses; (iii) inadequate use of existing algorithms for damage feature extraction because of neglecting the non-linearity and non-stationarity of the recorded signals; (iv) environmental and operational conditions can also generate false damage detections in bridges; (v) the drawbacks of traditional algorithms for processing large amounts of data obtained from the BHM. This thesis proposes new vibration-based parameters and methods with focus on damage detection, localization and quantification, considering a mixed robust methodology that includes signal processing and machine learning methods to solve the identified problems. The increasing volume of bridge monitoring data makes it interesting to study the ability of advanced tools and systems to extract useful information from dynamic and static variables. In the field of Machine Learning (ML) and Artificial Intelligence (AI), powerful algorithms have been developed to face problems where the amount of data is much larger (big data). The possibilities of ML techniques (unsupervised algorithms) were analyzed here in bridges taking into account both operational and environmental conditions. A critical literature review was performed and a deep study of the accuracy and performance of a set of algorithms for detecting damage in three real bridges and one numerical model. In the literature review inherent to the vibration-based damage detection, several state-of-the-art methods have been studied that do not consider the nature of the data and the characteristics of the applied excitation (possible non-linearity, non-stationarity, presence or absence of environmental and/or operational effects) and the noise level of the sensors. Besides, most research uses modal-based damage characteristics that have some limitations. A poor data normalization is performed by the majority of methods and both operational and environmental variability is not properly accounted for. Likewise, the huge amount of data recorded requires automatic procedures with proven capacity to reduce the possibility of false alarms. On the other hand, many investigations have limitations since only numerical or laboratory cases are studied. Therefore, a methodology is proposed by the combination of several algorithms to avoid them. The conclusions show a robust methodology based on ML algorithms capable to detect, localize and quantify damage. It allows the engineers to verify bridges and anticipate significant structural damage when occurs. Moreover, the proposed non-modal parameters show their feasibility as damage features using ambient and forced vibrations. Hilbert-Huang Transform (HHT) in conjunction with Marginal Hilbert Spectrum and Instantaneous Phase Difference shows a great capability to analyze the nonlinear and nonstationary response signals for damage identification under operational conditions. The proposed strategy combines algorithms for signal processing (ICEEMDAN and HHT) and ML (k-means) to conduct damage detection and localization in bridges by using the traffic-induced vibration data in real-time operation.En los últimos años la comunidad científica ha desarrollado nuevas técnicas en monitoreo de salud estructural (SHM) para identificar los daños en estructuras civiles especialmente en puentes. Los sistemas de monitoreo de puentes (BHM) sirven para reducir los costos generales de mantenimiento del ciclo de vida, ya que su principal objetivo es prevenir daños y fallas catastróficas. En el BHM que utiliza datos dinámicos, existen varios problemas relacionados con el procesamiento posterior de las señales de vibración, tales como: (i) cuando se utilizan características dinámicas modales como frecuencias naturales, formas de modos y amortiguamiento, presentan una limitación en relación con la localización del daño, ya que se basan en una respuesta global de la estructura; (ii) presencia de ruido en la medición de las respuestas de vibración; (iii) uso inadecuado de los algoritmos existentes para la extracción de características de daño debido a la no linealidad y la no estacionariedad de las señales registradas; (iv) las condiciones ambientales y operativas también pueden generar falsas detecciones de daños en los puentes; (v) los inconvenientes de los algoritmos tradicionales para procesar grandes cantidades de datos obtenidos del BHM. Esta tesis propone nuevos parámetros y métodos basados en vibraciones con enfoque en la detección, localización y cuantificación de daños, considerando una metodología robusta que incluye métodos de procesamiento de señales y aprendizaje automático. El creciente volumen de datos de monitoreo de puentes hace que sea interesante estudiar la capacidad de herramientas y sistemas avanzados para extraer información útil de variables dinámicas y estáticas. En el campo del Machine Learning (ML) y la Inteligencia Artificial (IA) se han desarrollado potentes algoritmos para afrontar problemas donde la cantidad de datos es mucho mayor (big data). Aquí se analizaron las posibilidades de las técnicas ML (algoritmos no supervisados) teniendo en cuenta tanto las condiciones operativas como ambientales. Se realizó una revisión crítica de la literatura y se llevó a cabo un estudio profundo de la precisión y el rendimiento de un conjunto de algoritmos para la detección de daños en tres puentes reales y un modelo numérico. En la revisión de literatura se han estudiado varios métodos que no consideran la naturaleza de los datos y las características de la excitación aplicada (posible no linealidad, no estacionariedad, presencia o ausencia de efectos ambientales y/u operativos) y el nivel de ruido de los sensores. Además, la mayoría de las investigaciones utilizan características de daño modales que tienen algunas limitaciones. Estos métodos realizan una normalización deficiente de los datos y no se tiene en cuenta la variabilidad operativa y ambiental. Asimismo, la gran cantidad de datos registrados requiere de procedimientos automáticos para reducir la posibilidad de falsas alarmas. Por otro lado, muchas investigaciones tienen limitaciones ya que solo se estudian casos numéricos o de laboratorio. Por ello, se propone una metodología mediante la combinación de varios algoritmos. Las conclusiones muestran una metodología robusta basada en algoritmos de ML capaces de detectar, localizar y cuantificar daños. Permite a los ingenieros verificar puentes y anticipar daños estructurales. Además, los parámetros no modales propuestos muestran su viabilidad como características de daño utilizando vibraciones ambientales y forzadas. La Transformada de Hilbert-Huang (HHT) junto con el Espectro Marginal de Hilbert y la Diferencia de Fase Instantánea muestran una gran capacidad para analizar las señales de respuesta no lineales y no estacionarias para la identificación de daños en condiciones operativas. La estrategia propuesta combina algoritmos para el procesamiento de señales (ICEEMDAN y HHT) y ML (k-means) para detectar y localizar daños en puentes mediante el uso de datos de vibraciones inducidas por el tráfico en tiempo real.Postprint (published version

A hybrid ensemble forecasting model of passenger flow based on improved variational mode decomposition and boosting

An accurate passenger flow forecast can provide key information for intelligent transportation and smart cities, and help promote the development of smart cities. In this paper, a mixed passenger flow forecasting model based on the golden jackal optimization algorithm (GJO), variational mode decomposition (VMD) and boosting algorithm was proposed. First, the data characteristics of the original passenger flow sequence were extended. Second, an improved variational modal decomposition method based on the Sobol sequence improved GJO algorithm was proposed. Next, according to the sample entropy of each intrinsic mode function (IMF), IMF with similar complexity is combined into a new subsequence. Finally, according to the determination rules of the sub-sequence prediction model, the boosting modeling and prediction of different sub-sequences were carried out, and the final passenger flow prediction result was obtained. Based on the experimental results of three scenic spots, the mean absolute percentage error (MAPE) of the mixed set model is 0.0797, 0.0424 and 0.0849, respectively. The fitting degree reached 95.33%, 95.63% and 95.97% simultaneously. The results show that the hybrid model proposed in this study has high prediction accuracy and can provide reliable information sources for relevant departments, scenic spot managers and tourists

Data analytics for mobile traffic in 5G networks using machine learning techniques

This thesis collects the research works I pursued as Ph.D. candidate at the Universitat Politecnica de Catalunya (UPC). Most of the work has been accomplished at the Mobile Network Department Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). The main topic of my research is the study of mobile network traffic through the analysis of operative networks dataset using machine learning techniques. Understanding first the actual network deployments is fundamental for next-generation network (5G) for improving the performance and Quality of Service (QoS) of the users. The work starts from the collection of a novel type of dataset, using an over-the-air monitoring tool, that allows to extract the control information from the radio-link channel, without harming the users’ identities. The subsequent analysis comprehends a statistical characterization of the traffic and the derivation of prediction models for the network traffic. A wide group of algorithms are implemented and compared, in order to identify the highest performances. Moreover, the thesis addresses a set of applications in the context mobile networks that are prerogatives in the future mobile networks. This includes the detection of urban anomalies, the user classification based on the demanded network services, the design of a proactive wake-up scheme for efficient-energy devices.Esta tesis recoge los trabajos de investigación que realicé como Ph.D. candidato a la Universitat Politecnica de Catalunya (UPC). La mayor parte del trabajo se ha realizado en el Centro Tecnológico de Telecomunicaciones de Catalunya (CTTC) del Departamento de Redes Móviles. El tema principal de mi investigación es el estudio del tráfico de la red móvil a través del análisis del conjunto de datos de redes operativas utilizando técnicas de aprendizaje automático. Comprender primero las implementaciones de red reales es fundamental para la red de próxima generación (5G) para mejorar el rendimiento y la calidad de servicio (QoS) de los usuarios. El trabajo comienza con la recopilación de un nuevo tipo de conjunto de datos, utilizando una herramienta de monitoreo por aire, que permite extraer la información de control del canal de radioenlace, sin dañar las identidades de los usuarios. El análisis posterior comprende una caracterización estadística del tráfico y la derivación de modelos de predicción para el tráfico de red. Se implementa y compara un amplio grupo de algoritmos para identificar los rendimientos más altos. Además, la tesis aborda un conjunto de aplicaciones en el contexto de redes móviles que son prerrogativas en las redes móviles futuras. Esto incluye la detección de anomalías urbanas, la clasificación de usuarios basada en los servicios de red demandados, el diseño de un esquema de activación proactiva para dispositivos de energía eficiente.Postprint (published version

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy