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A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks
Specifically, the aim of this study is to offer an introduction to the potential utilization of GenAI for critical thinking techniques in mobile networks, while also establishing a foundation for future research.
Using Large Language Models to Understand Telecom Standards
However, the volume and complexity of these standards has increased over time, thus complicating access to relevant information for vendors and service providers.
Multi-agent transformer-accelerated RL for satisfaction of STL specifications
One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases.
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Safe RAN control: A Symbolic Reinforcement Learning Approach
In particular, we provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology in order for the latter to execute optimal safe performance which is measured through certain Key Performance Indicators (KPIs).
Symbolic Reinforcement Learning for Safe RAN Control
In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL) architecture for safe control in Radio Access Network (RAN) applications.
Machine Reasoning Explainability
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning.
