Wireless Network
Wireless networks are undergoing a transformation driven by the integration of artificial intelligence, aiming to improve efficiency, reliability, and security in increasingly complex and resource-constrained environments. Current research focuses on optimizing network performance through techniques like federated learning (with adaptive quantization and client sampling), reinforcement learning (for resource allocation and UAV path planning), and the application of large language models and graph neural networks for intelligent network management and resource optimization. These advancements are crucial for enabling next-generation applications like the Metaverse and supporting the demands of the Internet of Things, impacting both the theoretical understanding of wireless systems and their practical deployment.
Papers
Graph Representation Learning for Contention and Interference Management in Wireless Networks
Zhouyou Gu, Branka Vucetic, Kishore Chikkam, Pasquale Aliberti, Wibowo Hardjawana
Joint Probability Selection and Power Allocation for Federated Learning
Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis, Nancy Alonistioti, M. A. Gutierrez-Estevez, Ramin Khalili