Advanced Machine Learning for 6G Networks - Doctoral Thesis Abstract

Aug 4, 2026

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Beyond 5G and 6G communications are expected to reshape connectivity for people, vehicles, wearables, devices, sensors, and physical/digital environments. The thesis applies advanced machine learning to core 6G challenges, focusing on UAVs for non-terrestrial networks, integrated communications and sensing for ubiquitous sensors, Metaverse resource management, and security against eavesdropping attacks. It proposes deep reinforcement transfer learning for energy-limited UAV learning, an adaptive ICAS waveform optimization framework for autonomous vehicles, a similarity-driven ML framework for Metaverse resource types, and ambient backscatter meta-learning to decode weak signals quickly under uncertainty. Results indicate strong potential and motivate future work on generative AI services over 6G.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages233

Reading Time9 h 47 min

AccessFree to download

Introduction and Literature Review

Motivations

Literature Review and Contributions

UAV-based data collection systems

Abstract

Acknowledgements

Table of Contents

Advanced Machine Learning for 6G Networks by Hoai Nam Chu Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy under the supervision of A/Prof. Diep N. Nguyen A/Prof. Hoang Dinh Prof. Eryk Dutkiewicz School of Electrical and Data Engineering Faculty of Engineering and IT University of Technology Sydney February 22, 2024 CERTIFICATE OF ORIGINAL AUTHORSHIP I, Hoai Nam Chu, declare that this thesis is submitted in fulfilment of the requirements for the award of Doctor of Philosophy, in the Faculty of Engineering and Information Technology at the University of Technology Sydney. This thesis is wholly my own work unless otherwise referenced or acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. This document has not been submitted for qualifications at any other academic institution. This research is supported by the Australian Government Research Training Program. Signature: Date: February 22, 2024 ABSTRACT Advanced Machine Learning for 6G Networks by Hoai Nam Chu Beyond 5G and 6G communications are foreseen to transform the world, connecting not only people but also vehicles, wearables, devices, sensors, and even physical and digital worlds. To achieve that, 6G systems are expected to employ various disruptive technologies (e.g., non-terrestrial networks (NTNs), mmWave communications, pervasive artificial intelligence, and ambient backscatter communications) to enable/support new use cases, e.g., autonomous cyber-physical systems and Metaverse/holographic teleportation. Thus, this thesis aims to leverage the latest advances in machine learning (ML) to address different problems facing 6G systems. We first envision that UAVs will play a critical role in 6G and NTNs, e.g., flying data collectors. To tackle the uncertainty in the data collection process and the UAV’s energy capacity limitation, we propose an innovative deep reinforcement transfer learning approach to control the UAV’s speed and energy replenishment process and allow UAVs to “share” and “transfer” learning knowledge, thus reducing learning time and improving learning quality significantly. 6G is also envisioned as ubiquitous sensors thanks to the Integrated Communications and Sensing (ICAS) technology, e.g., for flood sensing/warning or in autonomous vehicles (Avs) . Optimizing the waveform structure for ICAS applications to AVs is one of the most challenging tasks due to the strong influences between sensing and data communication functions under dynamic environments. Therefore, we develop a novel framework that intelligently and adaptively optimize its waveform structure to maximize sensing and data communication performance. Another key application/service of 6G is to enable the seamless deployment and operation of Metaverse. Building and maintaining the Metaverse not only demand enormous resources but also need to address the dynamic, uncertain, and real-time resource demands. Thus, we develop a novel ML-based framework that offers a highly effective and comprehensive solution for managing various resource types for Metaverse by leveraging the similarities among applications. Security is always one of the top concerns in wireless communications, especially for 6G connected by a massive number of heterogeneous devices. We design a lightweight framework leveraging ambient backscatter communications and deep meta-learning to counter eavesdropping attacks, effectively decode weak backscattered signals without requiring perfect information, and quickly adapt to new environments with very limited knowledge. The above results demonstrate the great potential of advanced machine learning in addressing the emerging issues of 6G and enabling new applications/services. As future works, one may look into the applications of Generative AI to 6G and how to design 6G systems to enable Generative AI as a service. Acknowledgements

FAQs

What is the main goal of this thesis on 6G networks?

It leverages advanced machine learning to address multiple problems in 6G systems and support emerging applications and services.

How does the thesis handle UAV data collection uncertainty and limited energy?

It introduces a deep reinforcement transfer learning approach that controls UAV speed and energy replenishment while transferring learning knowledge to reduce training time and improve quality.

What approach is proposed for integrated communications and sensing (ICAS) in dynamic environments?

The thesis develops an adaptive framework that optimizes ICAS waveform structure to maximize sensing and data communication performance despite strong sensing–communication coupling.

Aug 4, 2026

Upload by Oliver