13 papers
Multi-Modal Intelligent Channel Modeling: From Fine-tuned LLMs to Pre-trained Foundation Models
Lu Bai, Zengrui Han, Mingran Sun +1
To meet the evolving demands of sixth-generation (6G) wireless channel modeling, such as precise prediction capability, extension capabilities, and system participation capability,…
Multi-Modal Sensing Residual-Corrected GNN for mmWave Path Loss Prediction via Synesthesia of Machines
Mengyuan Lu, Lu Bai, Xiang Cheng
To support sixth-generation (6G)-enabled intelligent transportation systems (ITSs), a multi-modal sensing residual-corrected graph neural network (MM-ResGNN) framework is proposed…
WiCo-PG: Wireless Channel Foundation Model for Pathloss Map Generation via Synesthesia of Machines
Mingran Sun, Lu Bai, Ziwei Huang +3
A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncre…
WiCo-MG: Wireless Channel Foundation Model for Multipath Generation via Synesthesia of Machines
Zengrui Han, Lu Bai, Xuesong Cai +1
Precise modeling of channel multipath is essential for understanding wireless propagation environments and optimizing communication systems. In particular, sixth-generation (6G) ar…
SynthSoM-Twin: A Multi-Modal Sensing-Communication Digital-Twin Dataset for Sim2Real Transfer via Synesthesia of Machines
Junlong Chen, Ziwei Huang, Xuesong Cai +2
This paper constructs a novel multi-modal sensing-communication digital-twin dataset, named SynthSoM-Twin, which is spatio-temporally consistent with the real world, for Sim2Real t…
LLM4PG: Adapting Large Language Model for Pathloss Map Generation via Synesthesia of Machines
Mingran Sun, Lu Bai, Xiang Cheng +1
In this paper, a novel large language model (LLM)-based pathloss map generation model, termed LLM4PG, is proposed for sixth-generation (6G) AI-native communication systems via Syne…