7 papers
Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting
Ye Xue, Yiheng Wang, Xinhua Shao +3
Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), wh…
RadCloudSplat: Scatterer-Driven 3D Gaussian Splatting with Point-Cloud Priors for Radiomap Extrapolation
Yiheng Wang, Ye Xue, Shutao Zhang +2
A radiomap represents the spatial distribution of wireless signal strength, which is critical for applications like network optimization. However, constructing a radiomap relies on…
PEMNet: Towards Autonomous and Enhanced Environment-Aware Mobile Networks
Lei Li, Yanqing Xu, Ye Xue +4
With 5G deployment and the evolution toward 6G, mobile networks must make decisions in highly dynamic environments under strict latency, energy, and spectrum constraints. Achieving…
RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization
Bingsheng Peng, Shutao Zhang, Xi Zheng +3
Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localiz…
A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling
Xinyu Qin, Ye Xue, Qi Yan +3
Localized statistical channel modeling (LSCM) is crucial for effective performance evaluation in digital twin-assisted network optimization. Solely relying on the multi-beam refere…
Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling
Yiheng Wang, Shutao Zhang, Ye Xue +1
This paper presents MM-LSCM, a self-supervised multi-modal neural radio radiance field framework for localized statistical channel modeling (LSCM) for next-generation network optim…