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

Eye(I) Still Know! – An App for the Blind Built using Web and AI

This paper proposes eye(I) still know!, a voice control solution for the visually impaired people. The main purpose is even though the blind cannot see they can still know where to go and what to do! Nearby 60% of total blind population across the world is present in India. In a time where no one likes to rely on anyone, this is a small effort to make the blind independent individuals. This can be achieved using wireless communication, voice recognition and image scanning. The application with the use of object identification will priorly inform about the barriers in the path. The software will use the camera of the device and scan all the obstacles with their corresponding distances from the user. This will be followed by audio instructions through audio output of the device. This will efficiently direct the user through his/her way

Exploiting optical signal analysis for autonomous communications

(English) Optical communications have been extensively investigated and enhanced in the last decades. Nowadays, they are responsible to transport all the data traffic generated around the world, from access to the core network segments. As the data traffic is increasing and changing in both type and patterns, the optical communications networks and systems need to readapt and continuous advances to face the future data traffic demands in an efficient and cost-effective way. This PhD thesis focuses on investigate and analyze the optical signals in order to extract useful knowledge from them to support the autonomous lightpath operation, as well as to lightpath characterization. The first objective of this PhD thesis is to investigate the optical transmission feasibility of optical signals based on high-order modulation formats (MF) and high symbol rates (SR) in hybrid filterless, filtered and flexible optical networks. It is expected a higher physical layer impairments impact on these kinds of optical signals that can lead to degradation of the quality of transmission. In particular, the impact of the optical filter narrowing arising from the node cascade is evaluated. The obtained simulation results for the required optical-signal-to-noise ratio in a cascade up to 10 optical nodes foresee the applicability of these kinds of optical signals in such scenarios. By using high-order MF and high SR, the number of the optical transponders cab be reduced, as well as the spectral efficiency is enhanced. The second objective focuses on MF and SR identification at the optical receiver side to support the autonomous lightpath operation. Nowadays, optical transmitters can generate several optical signal configurations in terms of MF and SR. To increase the autonomous operation of the optical receiver, it is desired it can autonomously recognize the MF and SR of the incoming optical signals. In this PhD thesis, we propose an accurate and low complex MF and SR identification algorithm based on optical signal analysis and minimum Euclidean distance to the expected points when the received signals are decoded with several available MF and SR. The extensive simulation results show remarkable accuracy under several realistic lightpath scenarios, based on different fiber types, including linear and nonlinear noise interference, as well as in single and multicarrier optical systems. The final objective of this PhD thesis is the deployment of a machine learning-based digital twin for optical constellations analysis and modeling. An optical signal along its lightpath in the optical network is impaired by several effects. These effects can be linear, e.g., the noise coming from the optical amplification, or nonlinear ones, e.g., the Kerr effects from the fiber propagation. The optical constellations are a good source of information regarding these effects, both linear and nonlinear. Thus, by an accurate and deep analysis of the received optical signals, visualized in optical constellations, we can extract useful information from them to better understand the several impacts along the crossed lightpath. Furthermore, by learning the different impacts from different optical network elements on the optical signal, we can accurately model it in order to create a partial digital twin of the optical physical layer. The proposed digital twin shows accurate results in modeled lightpaths including both linear and nonlinear interference noise, in several lightpaths configuration, i.e., based on different kind of optical links, optical powers and optical fiber parameters. In addition, the proposed digital twin can be useful to predict quality of transmission metrics, such as bit error rate, in typical lightpath scenarios, as well as to detect possible misconfigurations in optical network elements by cooperation with the software-defined networking controller and monitoring and data analytics agents.(Español) Las comunicaciones ópticas han sido ampliamente investigadas y mejoradas en las últimas décadas. En la actualidad, son las encargadas de transportar la mayoría del tráfico de datos que se genera en todo el mundo, desde el acceso hasta los segmentos de la red troncal. A medida que el tráfico de datos aumenta y cambia tanto en tipo como en patrones, las redes y los sistemas de comunicaciones ópticas necesitan readaptarse y avanzar continuamente para, de una manera eficiente y rentable, hacer frente a las futuras demandas de tráfico de datos. Esta tesis doctoral se centra en investigar y analizar las señales ópticas con el fin de extraer de ellas conocimiento útil para apoyar el funcionamiento autónomo de las conexiones ópticas, así como para su caracterización. El primer objetivo de esta tesis doctoral es investigar la viabilidad de transmisión de señales ópticas basadas en formatos de modulación de alto orden y altas tasas de símbolos en redes ópticas híbridas con y sin filtros. Se espera un mayor impacto de las degradaciones de la capa física en este tipo de señales ópticas que pueden conducir a la degradación de la calidad de transmisión. En particular, se evalúa el impacto de la reducción del ancho de banda del filtro óptico que surge tras atravesar una cascada de nodos. Los resultados de simulación obtenidos para la relación señal óptica/ruido requerida en una cascada de hasta 10 nodos ópticos prevén la aplicabilidad de este tipo de señales ópticas en tales escenarios. Mediante el uso de modulación de alto orden y altas tasas de símbolos, se reduce el número de transpondedores ópticos y se mejora la eficiencia espectral. El segundo objetivo se centra en la identificación de formatos de modulación y tasas de símbolos en el lado del receptor óptico para respaldar la operación autónoma de la conexión óptica. Para aumentar el funcionamiento autónomo del receptor óptico, se desea que pueda reconocer de forma autónoma la configuración de las señales ópticas entrantes. En esta tesis doctoral, proponemos un algoritmo de identificación de formatos de modulación y tasas de símbolos preciso y de baja complejidad basado en el análisis de señales ópticas cuando las señales recibidas se decodifican con varios formatos de modulación y tasas de símbolos disponibles. Los extensos resultados de la simulación muestran una precisión notable en varios escenarios realistas, basados en diferentes tipos de fibra, incluida la interferencia de ruido lineal y no lineal, así como en sistemas ópticos de portadora única y múltiple. El objetivo final de esta tesis doctoral es el despliegue de un gemelo digital basado en aprendizaje automático para el análisis y modelado de constelaciones ópticas. Una señal óptica a lo largo de su trayectoria en la red óptica se ve afectada por varios efectos, pueden ser lineales o no lineales. Las constelaciones ópticas son una buena fuente de información sobre estos efectos, tanto lineales como no lineales. Por lo tanto, mediante un análisis preciso y profundo de las señales ópticas recibidas, visualizadas en constelaciones ópticas, podemos extraer información útil de ellas para comprender mejor los diversos impactos a lo largo del camino propagado. Además, al aprender los diferentes impactos de los diferentes elementos de la red óptica en la señal óptica, podemos modelarla con precisión para crear un gemelo digital parcial de la camada física óptica. El gemelo digital propuesto muestra resultados precisos en conexiones que incluyen ruido de interferencia tanto lineal como no lineal, en varias configuraciones basados en diferentes tipos de enlaces ópticos, potencias ópticas y parámetros de fibra óptica. Además, el gemelo digital propuesto puede ser útil para predecir la calidad de las métricas de transmisión así como para detectar posibles errores de configuración en los elementos de la red óptica mediante la cooperación con el controlador de red, el monitoreo y agentes de análisis de datosPostprint (published version

Looking at faces in the wild

Recent advances in the face detection (FD) and recognition (FR) technology may give an impression that the problem of face matching is essentially solved, e.g. via deep learning models using thousands of samples per face for training and validation on the available benchmark data-sets. Human vision system seems to handle face localization and matching problem differently from the modern FR systems, since humans detect faces instantly even in most cluttered environments, and often require a single view of a face to reliably distinguish it from all others. This prompted us to take a biologically inspired look at building a cognitive architecture that uses artificial neural nets at the face detection stage and adapts a single image per person (SIPP) approach for face image matching

Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter

Leak localization in water distribution networks using a mixed model-based/data-driven approach

“The final publication is available at Springer via http://dx.doi.org/10.1016/j.conengprac.2016.07.006”This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.Peer ReviewedPostprint (author's final draft