7 papers
Large Wireless Foundation Models: Stronger over Bigger
Xiang Cheng, Boxun Liu, Xuanyu Liu +1
AI-communication integration is widely regarded as a core enabling technology for 6G. Most existing AI-based physical-layer designs rely on task-specific models that are separately…
Embodied Intelligent Wireless (EIW): Synesthesia of Machines Empowered Wireless Communications
Xiang Cheng, Weibo Wen, Haotian Zhang +4
The evolution toward the sixth-generation (6G) and beyond mobile communication systems is marked by a fundamental shift from merely connecting devices to enabling pervasive and emb…
LLM4AMC: Adapting Large Language Models for Adaptive Modulation and Coding
Xinyu Pan, Boxun Liu, Xiang Cheng +1
Adaptive modulation and coding (AMC) is a key technology in 5G new radio (NR), enabling dynamic link adaptation by balancing transmission efficiency and reliability based on channe…
WiFo-CF: Wireless Foundation Model for CSI Feedback
Xuanyu Liu, Shijian Gao, Boxun Liu +2
Deep learning-based channel state information (CSI) feedback schemes demonstrate strong compression capabilities but are typically constrained to fixed system configurations, limit…
Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration
Xiang Cheng, Boxun Liu, Xuanyu Liu +2
To support future intelligent multifunctional sixth-generation (6G) wireless communication networks, Synesthesia of Machines (SoM) is proposed as a novel paradigm for artificial in…
LLM4WM: Adapting LLM for Wireless Multi-Tasking
Xuanyu Liu, Shijian Gao, Boxun Liu +2
The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning base…