Multi-Tier Deep Reinforcement Learning for Non-Terrestrial Networks
To provide global coverage and ubiquitous wireless services, non-terrestrial networks
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
Collaborative computing in non-terrestrial networks: A multi-time-scale deep reinforcement learning approach
Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks
(NTNs) has been the most promising paradigm to enable global coverage. The limited …
(NTNs) has been the most promising paradigm to enable global coverage. The limited …
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as
promising remedies to support global ubiquitous wireless services. Due to the rapid mobility …
promising remedies to support global ubiquitous wireless services. Due to the rapid mobility …
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 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 …
Deep reinforcement learning-based spectrum allocation in integrated access and backhaul networks
We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
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 …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
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 …
Multi-agent deep reinforcement learning for interference-aware channel allocation in non-terrestrial networks
Y Cho, W Yang, D Oh, HS Jo - IEEE Communications Letters, 2023 - ieeexplore.ieee.org
Non-terrestrial network (NTN) services using low-Earth-orbit (LEO) satellites are expanding.
Interference management of NTN services with other terrestrial wireless services is …
Interference management of NTN services with other terrestrial wireless services is …