Framework for Federated Learning and Edge Deployment of Real-Time Reinforcement Learning Decision Engine on Software Defined Radio
J Jagannath - Proceedings of the AAAI Symposium Series, 2024 - ojs.aaai.org
Abstract Machine learning promises to empower dynamic resource allocation requirements
of Next Generation (NextG) wireless networks including 6G and tactical networks. Recently …
of Next Generation (NextG) wireless networks including 6G and tactical networks. Recently …
Mr-inet gym: Framework for edge deployment of deep reinforcement learning on embedded software defined radio
Dynamic resource allocation plays a critical role in the next generation of intelligent wireless
communication systems. Machine learning has been leveraged as a powerful tool to make …
communication systems. Machine learning has been leveraged as a powerful tool to make …
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 …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Grgym: A playground for research on rl/ai enhanced wireless networks
A Zubow, S Roesler, P Gawlowicz… - … Wireless 2022; 27th …, 2022 - ieeexplore.ieee.org
The provision of a wide range of services each with different requirements makes next
generation wireless networks become more complex and heterogeneous which is aimed to …
generation wireless networks become more complex and heterogeneous which is aimed to …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Federated Multi Agent Deep Reinforcement Learning for Optimized Design of Future Wireless Networks
Federated Multi-Agent Deep Reinforcement Learning (F-MADRL) is gathering keen
research interests, as it may offer efficient solutions towards meeting the extreme …
research interests, as it may offer efficient solutions towards meeting the extreme …
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 …
Introduction to the special section on deep reinforcement learning for future wireless communication networks
We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Green deep reinforcement learning for radio resource management: Architecture, algorithm compression, and challenges
Artificial intelligence (AI) heralds a step-change in wireless networks but may also cause
irreversible environmental damage due to its high energy consumption. Here, we address …
irreversible environmental damage due to its high energy consumption. Here, we address …