PhD Oral Exam - Nada Abdelkhalek, Electrical and Computer Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Spectrum is one of the most vital public resources, carrying wireless communications for mobile phones, satellites, and emergency services. Traditionally, spectrum has been exclusively licensed, resulting in occasional underutilization. However, with the rapid proliferation of wireless devices, spectrum congestion has become inevitable, especially in unlicensed networks such as the Internet of Things (IoT), vehicular, and Unmanned Aerial Vehicles (UAVs), where devices rely on a limited number of public frequency bands that may not support large-scale communications. This contradiction between licensed spectrum underutilization and unlicensed spectrum congestion necessitates a rethinking of spectrum allocation and management strategies. In this thesis, we draw inspiration from Cognitive Radio (CR) technology, which equips radio devices with capabilities such as perception, reasoning, and judgment, and extend it to “intelligent radio” that integrates both cognition and learning capabilities. Our work advocates a shift from traditional model-driven approaches that rely on domain knowledge and strong assumptions to data-driven methods that learn directly from raw data and constant interactions with the environment. With the support of Artificial Intelligence (AI), we design intelligent spectrum borrowing and spectrum-sharing techniques that enable wireless devices to operate opportunistically on licensed bands. Specifically, this thesis explores how AI can endow wireless devices with context-awareness, self-optimization, and self-management capabilities for tasks such as dynamic spectrum access, power management, resource allocation, and ensuring security. Additionally, we develop solutions and frameworks for self-sustaining wireless devices that leverage Energy Harvesting (EH), bringing us closer to the realization of green networks. Our AI-driven algorithms are designed with computational efficiency in mind to minimize the burden on resource-constrained devices.

To drive context-aware intelligence in large-scale cooperative networks, we develop various unsupervised Machine Learning (ML) approaches for spectrum sensing. Unlike existing methods, the proposed frameworks operate without the need for labeled data, prior knowledge of the radio environment, or cooperation between licensed and unlicensed users. The approach ensures robust spectrum sensing while minimizing computational overhead for unlicensed users with limited capabilities. Moreover, we investigate how dimensionality reduction can improve computational efficiency and model generalizability. We expand the use of unsupervised learning to hybrid CR networks to allow devices to detect all licensed network states, opening up new opportunities for dynamic spectrum access.

To improve spectrum reasoning and analysis, we introduce some of the first fully unsupervised, data-efficient deep representation learning frameworks. These frameworks are designed to learn effective and disentangled representations of radio environment data. We demonstrate their effectiveness in significantly enhancing spectrum gap detection in small-scale cooperative networks. Additionally, we tackle key challenges of unsupervised learning, such as sensitivity to initialization and the need for predefined cluster counts. In large-scale networks, we propose a generative deep representation model that not only learns efficient representations but also captures the distribution of radio environment data, enabling the generation of new, unseen samples.

To facilitate edge intelligence and enhance the privacy of intelligent radios, we propose the first fully unsupervised deep Federated Learning (FL) framework for secure and distributed spectrum sensing in large-scale mobile networks. By leveraging user mobility across a large geographical area, the method enhances spatio-temporal diversity without requiring the transmission of private data to a central unit for processing. Instead, data is collected locally, and a shared model is collaboratively trained in a decentralized manner, significantly reducing communication overhead and safeguarding user privacy.

We tackle the growing challenge of spectrum scarcity in Cognitive IoT (CIoT) networks, where the demand for spectrum is increasing due to the expansion of connected devices. To address this, we develop intelligent and adaptive control algorithms for the joint management of network resources in spectrum-sharing environments. First, we formulate optimization problems under various constraints and model the decision-making process of a CIoT agent in the dynamic radio environment. We then propose two novel Deep Reinforcement Learning (DRL) algorithms that enable devices to autonomously learn operational strategies to optimize network resources and maximize long-term throughput without comprehensive prior knowledge. Additionally, we introduce innovative exploration strategies to enhance the CIoT agent's ability to identify optimal actions that maximize data rates. Considering the resource limitations of these networks, the algorithms are designed to be lightweight to reduce computational burdens on users. We also integrate EH techniques, such as Wireless Power Transfer (WPT) and Simultaneous Wireless Information and Power Transfer (SWIPT), to make these networks self-sustaining.

Finally, to develop dynamic strategies for navigating hostile spectrum-sharing environments impacted by jamming attacks, we propose an intelligent DRL approach that does not rely on frequency hopping. This algorithm is designed for rapid convergence, energy efficiency, and adaptability to adversarial conditions. We begin by formulating the optimization problem of power control under various constraints and modeling the decision-making process of the CIoT agent in such a hostile environment. Then, we introduce a novel interference-aware exploration strategy that enables the CIoT device to autonomously learn a transmission strategy, effectively mitigating jamming attacks and maximizing performance. Furthermore, we leverage WPT EH to allow the CIoT agent to convert jamming interference into a valuable resource for recharging.

In summary, the contributions of this thesis lay the foundation for a new generation of intelligent, autonomous wireless networks that are both spectrum-aware and agile, capable of optimizing resources and adapting to dynamic and complex environments.