Pre-Prints and Submissions

S. S. Hassan, I. A. Meer, A. Saifaldawla, Y. K. Tun, M. Ozger, M. Alsenwi, N. V. Huynh, W.-H. Lee, C. Stefanovic, M. Sellathurai, H. Wymeersch, and T. Ratnarajah, “JEPA for AI-Native 6G: Predictive Representations and Open Challenges,” arXiv, and submitted to IEEE Communications Magazine, Jul. 2026

J. Sajid, S. S. Hassan, W. Liu, Y. K. Tun, Y. Fu, N. H. Tran, Z. Han, C. Stefanovic, T. Ratnarajah, and M. M. Alam, “Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges,” arXiv and submitted to IEEE Communications Magazine, May 2026.

L. Zou, S. S. Hassan, A. Adhikary, Y. K. Tun, M. Debbah, Z. Han, and H. Oh, “Large Language Model-Native 6G Networks: A Survey of Architectures, Enablers, Challenges, and Opportunities,” submitted to IEEE Communications Surveys & Tutorials, May 2026.

L. U. Khan, W. Ullah, M. Guizani, S. S. Hassan, A. M. Khattak, and S. O. Gilani, “Optimization-Empowered Deep Reinforcement Learning for Metaverse: Fundamentals, Use Cases, and Open Challenges,” submitted to IEEE Internet of Things Magazine, May 2026.

S. S. Hassan, Y. M. Park, Y. K. Tun, Z. Han, C. S. Hong, J. S. Thompson, and T. Ratnarajah, "Hybrid Active-Passive RIS-Mounted UAVs for Enhanced Connectivity of Remote Users in 6G Networks", TechRxiv and submission to IEEE Systems Journal, Apr. 2026

N. Das, A. Adhikary, S. S. Hassan, Y. Qiao, T. Ratnarajah, Z. Han, and C. S. Hong, “A Novel Edge-Assisted Quantum-Classical Hybrid Framework for Crime Pattern Learning and Classification,” arXiv and submitted to IEEE GLOBECOM 2026.

M. Rahaman, A. Adhikary, T. Debnath, Y. Qiao, S. S. Hassan, M. S. Ahsan, W. Saad, D. Niyato, Z. Han, and C. S. Hong, "ISAC Meets LLMs: Advancing from Sensing to Reasoning with Foundation Models and Flexible Intelligent Metasurfaces", TechRxiv and submitted to IEEE Communications Surveys & Tutorials, Feb. 2026.

S. S. Hassan, Y. M. Park, Y. K. Tun, W. Saad, Z. Han, T. Ratnarajah, and C. S. Hong, "Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning", arXiv and submitted to IEEE Transactions on Vehicular Technology, Jan. 2026.

U. Majeed, S. S. Hassan, Z. Han, and C. S. Hong, “DAO-FL: Enabling Decentralized Input and Output Verification in Federated Learning with Decentralized Autonomous Organizations”, TechRxiv.