1 citations · 1 across the 4 of their papers we have counts for
6 papers
A Lightweight Hybrid Framework for Acoustic Echo Cancellation and Suppression via Constrained Recursive Training
Huawei Zhang, Rilin Chen, Hao Zhang +2
We propose a hybrid framework that combines deep learning with adaptive filtering for acoustic echo cancellation (AEC) and suppression. It integrates a multi-output neural network…
Hybrid spatial-temporal graph neural network Powered NDTs:Towards Next-Gen Smart Infrastructure Twins
John Sengendo, Fabrizio Granelli
Network Digital Twins (NDTs) enable proactive network management and optimization by predicting system behavior before control actions are applied to live infrastructures, supporti…
ST-NDT: A Topological Framework for Reducing Communication Overhead in Network Digital Twins
John Sengendo, Fabrizio Granelli
Next-generation networks (NGN) are becoming increasingly complex, especially with the increasing size of topologies. To manage this complexity requires the adoption of frameworks t…
Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting
John Sengendo, Zineddine Bettouche, Khalid Ali +2
As mobile networks transition from Beyond 5G (B5G) towards 6G, accurate traffic forecasting is a prerequisite for improving network management. However, with increasing heterogenei…
AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G
John Sengendo, Fabrizio Granelli
As 5G and future 6G mobile networks become increasingly more sophisticated, the requirements for agility, scalability, resilience, and precision in real-time service provisioning c…
Building Network Digital Twins Part II: Real-Time Adaptive PID for Enhanced State Synchronization
John Sengendo, Fabrizio Granelli
As we evolve towards more heterogeneous and cutting-edge mobile networks, Network Digital Twins (NDTs) are proving to be a promising paradigm in solving challenges faced by network…