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

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5 papers · 1 filter

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…