Icran: intelligent control for self-driving ran based on deep reinforcement learning
AH Ahmed, A Elmokashfi - IEEE Transactions on Network and …, 2022 - ieeexplore.ieee.org
Mobile networks are increasingly expected to support use cases with diverse performance
expectations at a very high level of reliability. These expectations imply the need for …
expectations at a very high level of reliability. These expectations imply the need for …
FORLORN: A framework for comparing offline methods and reinforcement learning for optimization of RAN parameters
V Edvardsen, G Spreemann… - Proceedings of the 18th …, 2022 - dl.acm.org
The growing complexity and capacity demands for mobile networks necessitate innovative
techniques for optimizing resource usage. Meanwhile, recent breakthroughs have brought …
techniques for optimizing resource usage. Meanwhile, recent breakthroughs have brought …
RIC: A RAN intelligent controller platform for AI-enabled cellular networks
B Balasubramanian, ES Daniels… - IEEE Internet …, 2021 - ieeexplore.ieee.org
With the emergence of 5G, network densification, and richer and more demanding
applications, the radio access network (RAN)—a key component of the cellular network …
applications, the radio access network (RAN)—a key component of the cellular network …
Self-optimization of cellular networks using deep reinforcement learning with hybrid action space
Wireless networks have been going through tremendous proliferation recently. As a result, a
continuous configuration and management are necessary to sustain a balanced …
continuous configuration and management are necessary to sustain a balanced …
Safe and accelerated deep reinforcement learning-based O-RAN slicing: A hybrid transfer learning approach
AM Nagib, H Abou-Zeid… - IEEE Journal on Selected …, 2023 - ieeexplore.ieee.org
The open radio access network (O-RAN) architecture supports intelligent network control
algorithms as one of its core capabilities. Data-driven applications incorporate such …
algorithms as one of its core capabilities. Data-driven applications incorporate such …
Toward safe and accelerated deep reinforcement learning for next-generation wireless networks
Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the
wireless networks domain. They are considered promising approaches for solving dynamic …
wireless networks domain. They are considered promising approaches for solving dynamic …
A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for
realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in …
realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in …
Learning driven mobility control of airborne base stations in emergency networks
Mobile base stations mounted on unmanned aerial vehicles (UAVs) provide viable wireless
coverage solutions in challenging landscapes and conditions, where cellular/WiFi …
coverage solutions in challenging landscapes and conditions, where cellular/WiFi …
Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …
mobile data traffic and support an increasingly high density of mobile users involving a …
Explora: Ai/ml explainability for the open ran
The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …
system of disaggregated, virtualized, and software-based components. These self-optimize …