Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

AI/ML Techniques for 5G Coverage Drop Prediction

This project introduces Machine Learning techniques to the wireless communication environment. In particular, the objective is to predict when the 5G network will not give coverage to a certain User Equipment. A sort of different techniques could be used to solve this, but the solution provided in this document uses a special type of Artificial Neural Network: The Echo State Network. As will be seen through the document, the project will focus on predicting the 5G Quality Parameter, and by introducing the Artificial Neural Networks it is expected for the solution to be able to predict not just the immediate next value, but the next five to ten values. On the results section a comparison between how the final model predicts the 5G Quality Parameter on different time instances will be provided. Finally, on the conclusions, the objective accomplishment will be discussed.Este proyecto tiene como objetivo introducir técnicas de Machine Learning al entorno de las comunicaciones inalámbricas. En particular, se quieren predecir esos momentos en los que la red 5G es incapaz de dar cobertura a un cierto usuario. Muchas técnicas, tanto dentro como fuera del Machine Learning, se podrían haber usado, pero la solución propuesta en este documento se basa en un tipo concreto de Red Neuronal Artificial: las Echo State Network. Como se podrá observar a lo largo del documento, el objetivo será predecir el nivel de calidad futuro de la red 5G. Haciendo que el modelo esté basado en una Red Neuronal Artificial, se espera que pueda ser capaz de predecir no sólo el valor posterior al recibido, sino también de los cinco a los diez valores posteriores. En la sección de Resultados, diversas comparativas entre la predicción del modelo y el valor real se harán, y finalmente, en la sección de Conclusiones se hará una breve discusión sobre el complimiento de los diferentes objetivos del proyecto.Aquest projecte té com a objectiu introduir tècniques de Machine Learning en l’entorn de les comunicacions inalàmbriques. En concret, es vol predir quan la xarxa de 5G no serà capaç de donar cobertura a un cert usuari. Moltes tècniques, tant dins com fora del Machine Learning, es podrien haver utilitzat, però la solució donada en aquest document s’ha fet a partir d’un tipus de Xarxa Neuronal Artificial: les Echo State Network. Com es podrà veure a mesura que avancem pel document, l’objectiu serà predir el nivell de qualitat donat per la xarxa 5G. Introduint les xarxes neuronals artificials, el que es vol és que el model final sigui capaç de predir, no només l’instant posterior al valor rebut, sinó els cinc o inclús els deu futurs valors. A la secció de Resultats veurem diverses comparatives entre la predicció donada pel model i els valors reals. Finalment, a les conclusions es durà a terme una breu discussió sobre l’acompliment dels objectiu

Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, reasonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatial-temporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatial-temporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective

Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for both short-term and long-term forecasting? How can a denoising mechanism enhance prediction accuracy? How can robust machine learning models be built with limited data? How can out-of-distribution traffic data be used to improve the generalizability of prediction models? Based on extensive experiments, we propose a novel traffic forecast/prediction framework and associated models that integrate outlier management and noise reduction strategies, outperforming traditional machine learning models. Additionally, we suggest a transfer learning-based framework combined with a data augmentation technique to provide robust solutions with smaller datasets. Lastly, we propose a hybrid model with signal decomposition techniques to enhance model generalization for out-of-distribution data samples. We also brought the issue of cyber threats as part of our forecast research, acknowledging their substantial influence on traffic unpredictability and forecasting challenges. Our thesis presents a detailed exploration of cyber-attack detection, employing methods that have been validated using multiple benchmark datasets. Initially, we incorporated ensemble feature selection with ensemble classification to improve DDoS (Distributed Denial-of-Service) attack detection accuracy with minimal false alarms. Our research further introduces a stacking ensemble framework for classifying diverse forms of cyber-attacks. Proceeding further, we proposed a weighted voting mechanism for Android malware detection to secure Mobile Cyber-Physical Systems, which integrates the mobility of various smart devices to exchange information between physical and cyber systems. Lastly, we employed Generative Adversarial Networks for generating flow-based DDoS attacks in Internet of Things environments. By considering the impact of cyber-attacks on traffic volume and their challenges to traffic prediction, our research attempts to bridge the gap between traffic forecasting and cyber security, enhancing proactive management of networks and contributing to resilient and secure internet infrastructure