Reinforcement learning for adaptive resource allocation in fog RAN for IoT with heterogeneous latency requirements
A Nassar, Y Yilmaz - IEEE Access, 2019 - ieeexplore.ieee.org
In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio
access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless …
access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless …
Resource allocation in fog RAN for heterogeneous IoT environments based on reinforcement learning
A Nassar, Y Yilmaz - ICC 2019-2019 IEEE International …, 2019 - ieeexplore.ieee.org
Fog radio access network (F-RAN) has been recently proposed to satisfy the low-latency
communication requirements of Internet of Things (IoT) applications. We consider the …
communication requirements of Internet of Things (IoT) applications. We consider the …
Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks
GMS Rahman, T Dang, M Ahmed - Intelligent and Converged …, 2020 - ieeexplore.ieee.org
Fog Radio Access Networks (F-RANs) have been considered a groundbreaking technique
to support the services of Internet of Things by leveraging edge caching and edge …
to support the services of Internet of Things by leveraging edge caching and edge …
Reinforcement learning-based resource management model for fog radio access network architectures in 5G
NN Khumalo, OO Oyerinde, L Mfupe - IEEE Access, 2021 - ieeexplore.ieee.org
The need to cope with the continuously growing number of connected users and the
increased demand for mobile broadband services in the Internet of Things has led to the …
increased demand for mobile broadband services in the Internet of Things has led to the …
Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor–critic deep reinforcement learning
The cloud-based Internet of Things (IoT) develops rapidly but suffer from large latency and
backhaul bandwidth requirement, the technology of fog computing and caching has …
backhaul bandwidth requirement, the technology of fog computing and caching has …
Delay-aware resource allocation in fog-assisted IoT networks through reinforcement learning
Fog nodes in the vicinity of IoT devices are promising to provision low-latency services by
offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices …
offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices …
Task allocation in fog-aided mobile IoT by Lyapunov online reinforcement learning
Fog-aided mobile IoT is proposed to speed up service response by deploying fog nodes at
network edges. We investigate the task allocation in fog-aided mobile IoT networks, where …
network edges. We investigate the task allocation in fog-aided mobile IoT networks, where …
Latency-driven fog cooperation approach in fog radio access networks
Fog computing, evolves from the cloud and migrates the computing to the edge, is a
promising solution to meet the increasing demand for ultra-low latency services in wireless …
promising solution to meet the increasing demand for ultra-low latency services in wireless …
Deep reinforcement learning-based mode selection and resource management for green fog radio access networks
Fog radio access networks (F-RANs) are seen as potential architectures to support services
of Internet of Things by leveraging edge caching and edge computing. However, current …
of Internet of Things by leveraging edge caching and edge computing. However, current …
Deep reinforcement learning‐based joint optimization of computation offloading and resource allocation in F‐RAN
S Jo, U Kim, J Kim, C Jong, C Pak - IET communications, 2023 - Wiley Online Library
The fog radio access network (F‐RAN) has been regarded as a promising wireless access
network architecture in the fifth generation (5G) and beyond systems to satisfy the increasing …
network architecture in the fifth generation (5G) and beyond systems to satisfy the increasing …
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