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 …
A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives
Unmanned aerial vehicles (UAVs), also known as drones, communicate, collaborate, and
form flying ad hoc networks (FANETs) to perform many different missions, ranging from …
form flying ad hoc networks (FANETs) to perform many different missions, ranging from …
UAV-assisted IoT applications, cybersecurity threats, AI-enabled solutions, open challenges with future research directions
Unnamed Ariel Vehicle-assisted-Internet of Things (UAV-assisted IoT) applications have
emerged as a powerful integrated technology, showcasing remarkable results in many …
emerged as a powerful integrated technology, showcasing remarkable results in many …
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 …
[HTML][HTML] DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency
The limited coverage extension of mobile edge computing (MEC) necessitates exploring
cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future …
cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future …
[HTML][HTML] Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning
CK Hsieh, KL Chan, FT Chien - Applied Sciences, 2021 - mdpi.com
This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
Hierarchical multi-agent DRL-based framework for joint multi-RAT assignment and dynamic resource allocation in next-generation HetNets
This article considers the problem of cost-aware downlink sum-rate maximization via joint
optimal radio access technologies (RATs) assignment and power allocation in next …
optimal radio access technologies (RATs) assignment and power allocation in next …
[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 …
Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey
S Gupta, N Singh - International Journal of Communication …, 2023 - Wiley Online Library
Fog computing has already started to gain a lot of momentum in the industry for its ability to
turn scattered computing resources into a large‐scale, virtualized, and elastic computing …
turn scattered computing resources into a large‐scale, virtualized, and elastic computing …
[HTML][HTML] Iot-driven workflows for risk management and control of beehives
The internet of things (IoT) and Industry 4.0 technologies are becoming widely used in the
field of apiculture to enhance honey production and reduce colony losses using connected …
field of apiculture to enhance honey production and reduce colony losses using connected …