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

Unsupervised Clustering of RF-Fingerprinting Features Derived from Deep Learning Based Recognition Models

RF-Fingerprinting is focus of machine learning research which aims to characterize wireless communication devices based on their physical hardware characteristics. It is a promising avenue for improving wireless communication security in the PHY layer. The bulk of research presented to date in this field is focused on the development of features and classifiers using both traditional supervised machine learning models as well as deep learning. This research aims to expand on existing RF-Fingerprinting work by approaching the problem through the lens of an unsupervised clustering problem. To that end this research proposes a deep learning model and training methodology to extract features from IEEE 802.11a/g preamble waveforms to enhance performance with various clustering algorithms. The model architecture presented takes the form of convolutional autoencoder with an objective function that combines both autoencoder reconstruction loss as well as triplet loss to learn feature encodings. These features were then clustered using the K-means, DBSCAN, and Mean Shift clustering algorithms

A phase likelihood-based algorithm for blind identification of PSK signals

pre-printThis paper presents a phase likelihood-based method for automatically identifying different phase-shift keying (PSK) modulations. This method identifies the PSK signals as the hypothesis for which the likelihood function of phase difference between nearby samples of the received signal is the maximum. This method does not need prior knowledge of carrier frequency or baud rate and can identify modulation types at relatively low signal-to-noise ratio (SNR) and using small number of input samples. Simulation results demonstrate that this algorithm can identify BPSK, QPSK and 8PSK signals with 100% accuracy with only 1000 symbols when the SNR of the input signal is better than 7 dB. Additional simulation results demonstrating the robustness of the algorithm to variations of the noise characteristics from the assumed Gaussian model are also included in the paper. Performance comparisons indicate that the approach of this paper can achieve 100% accuracy in modulation identification at 5-7 dB lower SNR than competing methods available in the literature

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

Machine Learning Tips and Tricks for Power Line Communications

4openopenTonello A.M.; Letizia N.A.; Righini D.; Marcuzzi F.Tonello, A. M.; Letizia, N. A.; Righini, D.; Marcuzzi, F