14 citations · 14 across the 11 of their papers we have counts for
12 papers
A Foundation Model for Large-Scale Wireless Network Planning , Operation and Optimization
Xinyu Qin, Wenqiang Pu, Hongcheng Dong +6
Wireless cellular networks form the connective tissue of human society, sustained by a continuous physical dialogue between engineered infrastructure and its surroundings. Radio si…
Congruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment
Yeqing Qiu, Chengpiao Huang, Ye Xue +7
Physical Cell Identity (PCI) assignment is essential for interference management in dense 5G networks. As cellular networks scale, PCI reuse becomes unavoidable, which may cause co…
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…
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…
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…
ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems
Yeqing Qiu, Ye Xue, Akang Wang +3
The Max-k-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic NP-complete Max-Cut problem. While relaxation techniques are commonly emplo…
Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC
Ye Xue, An Liu, Yang Li +2
In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detec…
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation
Ye Xue, Vincent Lau
Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing th…
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method
Ye Xue, Vincent Lau
Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works of dictionary learning are in an offline manner. Th…