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Found 8 papers, 0 papers with code
Multi-Task Learning as enabler for General-Purpose AI-native RAN
The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the network edges driving downstream tasks like secondary carrier prediction, positioning, channel prediction etc.
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks
Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.
Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks
In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization.
Coordinated Reinforcement Learning for Optimizing Mobile Networks
In this work, we demonstrate how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions
The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before.
Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks
In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input.
Can Machine Learning Be Used to Recognize and Diagnose Coughs?
One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections.
