Wireless System
Wireless systems research currently focuses on optimizing resource allocation and improving the efficiency and reliability of wireless communication, particularly in the context of increasingly complex and data-intensive applications. Key areas of investigation include leveraging artificial intelligence, specifically large language models and deep learning, for tasks such as resource allocation, model selection, and channel estimation, often employing techniques like federated learning and digital twinning to address challenges in heterogeneous and dynamic environments. These advancements aim to enhance the performance and scalability of wireless networks, supporting the growing demands of applications like AI-generated content and real-time control systems.
Papers
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
George C. Alexandropoulos, Kyriakos Stylianopoulos, Chongwen Huang, Chau Yuen, Mehdi Bennis, Mérouane Debbah
Light Communication for Controlling Industrial Robots
Fadi Al-Turjman, Diletta Cacciagrano, Leonardo Mostarda, Mattia Paccamiccio, Zaib Ullah