PhD defense Abdelaziz Bounhar: Information Theory and Reinforcement Learning for Mixed Covert and Non-Covert Wireless Networks

Friday, 06 December, 2024 at 14.00 (Paris time) at Télécom Paris

Télécom Paris, 19 place Marguerite Perey F-91120 Palaiseau [getting there], amphi Estaunié and in videoconferencing

Jury

Mrs. Michèle WIGGER, Télécom Paris, Thesis director
Mrs. Mireille SARKISS, Télécom SudParis, Thesis co-director
Mr. Matthieu BLOCH, Georgia Institute of Technology, Reviewer
Mr. Philippe MARY, INSA de Renne, Reviewer
Mr. Deniz GÜNDÜZ, Imperial College London, Examiner
Mr. Ligong WANG, ETH Zurich, Examiner
Mrs. Haoyue TANG, Meta AI, Examiner
Mrs. Laura LUZZI, ENSEA, Examiner

Abstract

While cryptographic methods offer security, they are often impractical for Internet of Things (IoT) devices due to their limited computational resources and battery life. In light of these challenges, physical layer security techniques, particularly covert communication, seems to be an adequate solution for securing IoT communications. Existing research on covert communication has predominantly focused on systems with solely covert users. This thesis addresses this gap and pioneers the characterization of the information-theoretic fundamental limits of communication systems involving both covert and non-covert users, demonstrating how and when non-covert users can enhance covert communication.

It also advances previous findings...

… on the single and multi-users setup by characterizing the exact secret-key rate needed to communicate at a given covert data rate. In another line of work, we address the central approach to modern semantic and goal-oriented communication systems. Specifically, we address the joint source-channel coding problem under a covertness constraints, identifying optimal coding schemes that meet the covertness requirement. These theoretical insights are validated through deep learning techniques, showing that covert semantic communication is only guaranteed when the established theoretical constraints are met. Lastly, to further enrich our research, we extend our work to setups that encompass both covert and non-covert users operating using Non-Orthogonal Multiple Access in an Additive White Gaussian Noise channel. By leveraging reinforcement learning techniques, we develop efficient resource allocation policies that effectively optimize performance in these intricate environments, accounting for real-world constraints such as imperfect channel state information and energy limitations.