17 papers
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction
Shiliang Lu, Lu Bai, Ziwei Huang +1
For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpoints. We propose cross-image r…
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…
LLM4MG: Adapting Large Language Model for Multipath Generation via Synesthesia of Machines
Ziwei Huang, Shiliang Lu, Lu Bai +2
Based on Synesthesia of Machines (SoM), a large language model (LLM) is adapted for multipath generation (LLM4MG) for the first time. Considering a typical sixth-generation (6G) ve…
Multi-Modal Intelligent Channel Modeling Framework for 6G-Enabled Networked Intelligent Systems
Lu Bai, Zengrui Han, Xuesong Cai +1
The design and technology development of 6G-enabled networked intelligent systems needs an accurate real-time channel model as the cornerstone. However, with the new requirements o…
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…
LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines
Zengrui Han, Lu Bai, Ziwei Huang +1
In this paper, a novel large language model (LLM)-based method for scatterer generation (LLM4SG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communica…
A Multi-modal Intelligent Channel Model for 6G Multi-UAV-to-Multi-Vehicle Communications
Lu Bai, Mengyuan Lu, Ziwei Huang +1
In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-unmanned aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To…
Synesthesia of Machines Based Multi-Modal Intelligent V2V Channel Model
Zengrui Han, Lu Bai, Ziwei Huang +1
This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Syne…
SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)
Xiang Cheng, Ziwei Huang, Yong Yu +5
Given the importance of datasets for sensing-communication integration research, a novel simulation platform for constructing communication and multi-modal sensory dataset is devel…
Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines
Lu Bai, Ziwei Huang, Mingran Sun +2
In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understandi…
Scatterer Recognition for Multi-Modal Intelligent Vehicular Channel Modeling via Synesthesia of Machines
Ziwei Huang, Lu Bai, Zengrui Han +1
In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of…
A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration
Ziwei Huang, Lu Bai, Mingran Sun +1
In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed…