activity
20232026
most citedMSC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration

1 citations · 2 across the 18 of their papers we have counts for

collaborators

20 papers

eess.SP2026

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…

eess.SP2026

Sensing-Assisted LoS/NLoS Identification in Dynamic UAV Positioning Systems

Huijuan Qiao, Lu Bai, Mingran Sun +3

In this paper, a sensing-assisted non-line-of-sight (NLoS) identification method for dynamic uncrewed aerial vehicle (UAV) positioning is proposed for the first time. For urban UAV…

eess.SP2026

A Multi-Modal Intelligent U2V Channel Model for 6G Sensing-Communication Integration

Shuo Wang, Zengrui Han, Lu Bai +1

This paper proposes a novel UAV-to-Vehicle (U2V) channel model for sixth-generation (6G) intelligent sensing-communication integration, based on three-dimensional (3D) scatterer pr…

eess.SP2026

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,…

eess.SP2026

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

cs.CV2025

GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data

Lubian Bai, Xiuyuan Zhang, Siqi Zhang +4

Integrating ground-level geospatial data with rich geographic context, like OpenStreetMap (OSM), into remote sensing (RS) foundation models (FMs) is essential for advancing geospat…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2024

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…

eess.SP2024

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…

eess.SP2024

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…

eess.SP2023★ 1 cited

MSC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration

Xiang Cheng, Ziwei Huang, Lu Bai +6

The sixth generation (6G) of mobile communication system is witnessing a new paradigm shift, i.e., integrated sensing-communication system. A comprehensive dataset is a prerequisit…

eess.SP2023★ 1 cited

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

Xiang Cheng, Haotian Zhang, Jianan Zhang +7

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by en…