Coexistence of Wi-Fi and 5G NR-U in the Unlicensed Band

The communications industry continues to evolve to meet the ever-growing demands of fast connectivity and higher energy-efficiency and has emerged the concept of Internet of Things (IoT) systems. IoT devices can be run on Wi-Fi or cellular network, helping businesses to receive higher return on investments. As billions of devices on cellular networks operate on the limited licensed spectrum, it is becoming scarcer. Mobile network operators are investigating to access the immense unlicensed spectrum, on which Wi-Fi is prominently operated. Managing this coexistence between the cellular and Wi-Fi networks poses several challenges. One challenge is the spectrum sharing that affects the network capacity and the spectrum efficiency by properly allocating the available resources for each technology. A second challenge is to maintain the quality of service (QoS) while maximizing the aggregated throughput. A final challenge is to reduce the power consumption of cellular base stations by creating a sleep/wakeup policy, thereby lowering the capital and operating expenses for the mobile network operators. To this end, this thesis proposes various optimization modeling for the coexistence mechanisms in the unlicensed spectrum, as well as intelligent techniques to manage the increasing power consumption with increased usage. First, this thesis develops optimization modeling techniques to properly allocate resources for the coexistence of the Wi-Fi and cellular networks by improving the aggregate throughput, while maintaining the minimum required power consumption. Next, this thesis implements the coexistence mechanism by simulating real-time traffic information to maximize the aggregate throughput, while satisfying the QoS for each user. Finally, this thesis investigates the use of machine learning techniques to predict the traffic behaviour of base stations; this will determine the sleep/wakeup schedule, thereby minimizing the power consumption while maintaining the QoS for each cellular user

Leveraging Machine Learning Techniques towards Intelligent Networking Automation

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the computational costs of implementing the proposed mechanisms. Accordingly, this thesis tackles the challenges that four specific research problems present. The first topic addresses the problem of balancing traffic in dense Internet of Things (IoT) network scenarios where the end devices and the Base Stations (BSs) form complex networks. By applying ML techniques to discover patterns in the association between the end devices and the BSs, the proposed scheme can balance the traffic load in a IoT network to increase the packet delivery ratio and reduce the energy cost of data delivery. The second research topic proposes an intelligent congestion control for internet connections at edge network elements. The design includes a congestion predictor based on an Artificial Neural Network (ANN) and an Active Queue Management (AQM) parameter tuner. Similarly, the third research topic includes an intelligent solution to the inter-domain congestion. Different from second topic, this problem considers the preservation of the private network data by means of Federated Learning (FL), since network elements of several organizations participate in the intelligent process. Finally, the fourth research topic refers to a framework to efficiently gathering network telemetry (NT) data. The proposed solution considers a traffic-aware approach so that the NT is intelligently collected and transmitted by the network elements. All the proposed schemes are evaluated through use cases considering standardized networking mechanisms. Therefore, we envision that the solutions of these specific problems encompass a set of methods that can be utilized in real-world scenarios towards the realization of the INA paradigm

Optimization Modeling and Machine Learning Techniques Towards Smarter Systems and Processes

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is protecting such devices and systems given the surge in security breaches and attacks in recent times [5]. To that end, this thesis proposes the use of various optimization modeling and machine learning techniques in three different systems; namely wireless communication systems, learning management systems (LMSs), and computer network systems. In par- ticular, the first part of the thesis posits optimization modeling techniques to improve the aggregate throughput and power efficiency of a wireless communication network. On the other hand, the second part of the thesis proposes the use of unsupervised machine learning clustering techniques to be integrated into LMSs to identify unengaged students based on their engagement with material in an e-learning environment. Lastly, the third part of the thesis suggests the use of exploratory data analytics, unsupervised machine learning clustering, and supervised machine learning classification techniques to identify malicious/suspicious domain names in a computer network setting. The main contributions of this thesis can be divided into three broad parts. The first is developing optimal and heuristic scheduling algorithms that improve the performance of wireless systems in terms of throughput and power by combining wireless resource virtualization with device-to-device and machine-to-machine communications. The second is using unsupervised machine learning clustering and association algorithms to determine an appropriate engagement level model for blended e-learning environments and study the relationship between engagement and academic performance in such environments. The third is developing a supervised ensemble learning classifier to detect malicious/suspicious domain names that achieves high accuracy and precision