10 papers
ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation
Kangjun Liu, Xiying Pan, Shuhang Zhang +3
Radio maps describe how wireless signals propagate across space and are essential for wireless communication, sensing, and network planning. However, constructing accurate radio ma…
Local Margin Restoration for Test-Time Adaptation of Vision-Language Models
Yan Huang, Guowei Wang, Xu Wang +2
Vision-language models (VLMs) such as CLIP exhibit remarkable zero-shot capabilities, yet their performance frequently degrades sharply under unexpected test-time distribution shif…
RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling
Zheng Fang, Junjie Liu, Kangjun Liu +3
With the emergence of wireless applications in three-dimensional environments, such as the low-altitude airspace and 3D heterogeneous networks, radio map estimation is increasingly…
Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm
Feng Qiu, Zheng Fang, Shuhang Zhang +4
Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network optimization. Most current…
RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation
Taiqin Chen, Zikun Zhou, Zheng Fang +5
The radio map represents the spatial distribution of spectrum resources within a region, supporting efficient resource allocation and interference mitigation. However, it is diffic…
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
Longkun Zou, Kangjun Liu, Ke Chen +3
Learning semantic representations from point sets of 3D object shapes is often challenged by significant geometric variations, primarily due to differences in data acquisition meth…
RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1\textpertenthousand Spatial Sampling
Zheng Fang, Kangjun Liu, Ke Chen +4
The task of radio map estimation aims to generate a dense representation of electromagnetic spectrum quantities, such as the received signal strength at each grid point within a ge…
Delving Deep into Semantic Relation Distillation
Zhaoyi Yan, Kangjun Liu, Qixiang Ye
Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional dist…
ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points
Qirui Huang, Runze Zhang, Kangjun Liu +3
We introduce ArcPro, a novel learning framework built on architectural programs to recover structured 3D abstractions from highly sparse and low-quality point clouds. Specifically,…
Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps
Shuhang Zhang, Shuai Jiang, Wanjie Lin +4
Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the futu…