[HTML][HTML] Applications of multi-agent deep reinforcement learning: Models and algorithms
Recent advancements in deep reinforcement learning (DRL) have led to its application in
multi-agent scenarios to solve complex real-world problems, such as network resource …
multi-agent scenarios to solve complex real-world problems, such as network resource …
[HTML][HTML] Application of deep learning for quality of service enhancement in internet of things: A review
N Kimbugwe, T Pei, MN Kyebambe - Energies, 2021 - mdpi.com
The role of the Internet of Things (IoT) networks and systems in our daily life cannot be
underestimated. IoT is among the fastest evolving innovative technologies that are digitizing …
underestimated. IoT is among the fastest evolving innovative technologies that are digitizing …
Age of information aware VNF scheduling in industrial IoT using deep reinforcement learning
In delay-sensitive industrial Internet of Things (IIoT) applications, the age of information (AoI)
is employed to characterize the freshness of information. Meanwhile, the emerging network …
is employed to characterize the freshness of information. Meanwhile, the emerging network …
Virtualized network function forwarding graph placing in SDN and NFV-enabled IoT networks: A graph neural network assisted deep reinforcement learning method
Y Xie, L Huang, Y Kong, S Wang, S Xu… - … on Network and …, 2021 - ieeexplore.ieee.org
With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks
face a huge challenge which is providing diverse and complex network services with …
face a huge challenge which is providing diverse and complex network services with …
Service-aware resource orchestration in ultra-dense LEO satellite-terrestrial integrated 6G: A service function chain approach
With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-
dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising …
dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising …
Multi-agent deep reinforcement learning-empowered channel allocation in vehicular networks
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Channel allocation has a direct and profound impact on the performance of vehicle-to-
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
Relaying in the Internet of Things (IoT): A survey
The deployment of relays between Internet of Things (IoT) end devices and gateways can
improve link quality. In cellular-based IoT, relays have the potential to reduce base station …
improve link quality. In cellular-based IoT, relays have the potential to reduce base station …
Joint network control and resource allocation for space-terrestrial integrated network through hierarchal deep actor-critic reinforcement learning
Conventional approaches to network control and resource allocation by allocating dedicated
spectrum resources and separate infrastructure for massive Internet of Things (IoT) network …
spectrum resources and separate infrastructure for massive Internet of Things (IoT) network …
Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-based Deep Reinforcement Learning Approach
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power …
A-DDPG: Attention mechanism-based deep reinforcement learning for NFV
The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the
virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately …
virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately …