arXiv:2607. 16877v1 Announce Type: cross Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications.
By Yangjing Wang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
arXiv:2608. 14591v1 Announce Type: cross Abstract: The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems.
By Shugong Xu, Jun Jiang, Yuan Gao
Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstruction over subcarriers, antennas, or time but ignore the physical characteristics of wireless propagation.
arXiv:2608. 05076v1 Announce Type: cross Abstract: Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals.
By Blessed Guda, Kayley Sze, Carlee Joe-Wong
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed
arXiv:2606. 29640v1 Announce Type: cross Abstract: While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a critical barrier to deployment.
By Omar Mashaal, Hatem Abou-Zeid