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Found 4 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.
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.
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
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.
