30 citations · 40 across the 9 of their papers we have counts for
10 papers
Scene-Conditioned Relation Routing for urban cellular activity forecasting
Qingzhong Li, Jingye Lin, Hui Ma +4
Urban cellular activity forecasting requires jointly modeling heterogeneous spatiotemporal signals, including SMS usage, mobile network traffic, and call activity. Existing methods…
Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting
Qingzhong Li, Yue Hu, Hui Ma +4
Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Re…
GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction
Ziyi Li, Hui Ma, Fei Xing +2
With rapid expansion of cellular networks and the proliferation of mobile devices, cellular traffic data exhibits complex temporal dynamics and spatial correlations, posing challen…
AsynDBT: Asynchronous Distributed Bilevel Tuning for efficient In-Context Learning with Large Language Models
Hui Ma, Shaoyu Dou, Ya Liu +3
With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce usage costs. However, since cloud-based mod…
An Adaptive Differentially Private Federated Learning Framework
Jin Wang, Hui Ma, Yajun Zhang +4
Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments, device heterogeneity and non-in…
Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting
Hui Ma, Qingzhong Li, Jin Wang +4
Network traffic forecasting plays a crucial role in intelligent network operations, but existing techniques often perform poorly when faced with limited data. Additionally, multi-t…
Fog Intelligence for Network Anomaly Detection
Kai Yang, Hui Ma, Shaoyu Dou
Anomalies are common in network system monitoring. When manifested as network threats to be mitigated, service outages to be prevented, and security risks to be ameliorated, detect…
Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism
Hui Ma, Kai Yang, Man-On Pun
Cellular traffic prediction is of great importance for operators to manage network resources and make decisions. Traffic is highly dynamic and influenced by many exogenous factors,…
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
Hui Ma, Kai Yang
Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing predictio…
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
Hui Ma, Kai Yang, Yang Jiao
Network traffic prediction plays a crucial role in intelligent network operation. Traditional prediction methods often rely on centralized training, necessitating the transfer of v…