PhD Oral Exam - Ali Amhaz, Information and Systems 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

The exponential growth of wireless traffic, the rapid proliferation of connected devices, and the emergence of data-intensive intelligent applications have accelerated the advancement toward next-generation wireless networks, commonly known as sixth-generation (6G) networks. Envisioned as a major transition beyond current communication systems, 6G is expected to enable groundbreaking applications such as augmented reality, collaborative robotics, holographic telepresence, and even space and deep-sea tourism. Beyond supporting ultra-high data rates and massive connectivity, future 6G systems are expected to seamlessly integrate communication and sensing functionalities to enable applications such as autonomous transportation, collaborative robotics, extended reality, holographic telepresence, and smart industrial automation. These applications require wireless networks that can simultaneously deliver reliable communications, accurate environmental sensing, ultra-low latency, and high spectral and energy efficiency in highly dynamic environments. However, meeting these stringent and conflicting requirements introduces significant challenges related to spectrum scarcity, interference management, resource coupling, communication–sensing tradeoffs, and the resulting large-scale non-convex optimization problems.

Subsequently, this thesis develops unified optimization and learning frameworks for next-generation 6G wireless networks by jointly integrating advanced multiple access schemes, cooperative transmission strategies, sensing functionalities, and emerging hardware technologies to address the key challenges of massive connectivity, spectral efficiency, communication reliability, and integrated sensing and communication (ISAC). In particular, the work investigates the synergistic integration of UAV-enabled communications, cooperative non-orthogonal multiple access (C-NOMA), rate-splitting multiple access (RSMA), full-duplex (FD) transmission, coordinated multi-point (CoMP), and movable antenna (MA) technologies within unified system designs.

To efficiently handle the highly coupled and non-convex optimization problems arising from these architectures, the thesis develops a combination of advanced optimization and machine learning methodologies, including successive convex approximation (SCA), alternating optimization, and deep reinforcement learning approaches based on the deep deterministic policy gradient (DDPG) algorithm, as well as gradient-based meta-learning (GML) techniques for scalable large-scale optimization. These frameworks jointly optimize communication, sensing, beamforming, resource allocation, user pairing, UAV deployment and trajectory design, and antenna positioning under practical communication and sensing constraints.

The proposed frameworks demonstrate that tightly integrating cooperative transmission, ISAC capabilities, and adaptive antenna technologies along with next-generation multiple access techniques can substantially improve network throughput, spectral efficiency, sensing accuracy, coverage, and robustness against interference and channel impairments. Overall, the thesis establishes unified optimization-driven and learning-enabled design paradigms that highlight the potential of emerging multiple access and hardware technologies in realizing efficient, intelligent, and sensing-aware 6G wireless networks.