Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A Survey
NA Khalek, DH Tashman… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The next frontier in wireless connectivity lies at the intersection of cognitive radio (CR)
technology and machine learning (ML), where intelligent networks can provide pervasive …
technology and machine learning (ML), where intelligent networks can provide pervasive …
The applicability of reinforcement learning methods in the development of industry 4.0 applications
Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …
problems. Our article gives a systematic overview of major types of RL methods, their …
Edge intelligence (EI)-enabled HTTP anomaly detection framework for the Internet of Things (IoT)
In recent years, with the rapid development of the Internet of Things (IoT), various
applications based on IoT have become more and more popular in industrial and living …
applications based on IoT have become more and more popular in industrial and living …
Multi-agent task planning and resource apportionment in a smart grid
Nowadays, in different fields, tremendous attention is received by the Multi-agent systems for
complex problem solutions with smaller task subdivision. Multiple inputs are utilized, eg …
complex problem solutions with smaller task subdivision. Multiple inputs are utilized, eg …
Joint trajectory design and BS association for cellular-connected UAV: An imitation-augmented deep reinforcement learning approach
YJ Chen, DY Huang - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
This article concerns the problem of the trajectory design and base station (BS) association
for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions …
for cellular-connected unmanned aerial vehicles (UAVs). To support safety-critical functions …
A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network
Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth
of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can …
of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can …
A Review on Deep Reinforcement Learning for the management of SDN and NFV in Edge-IoT
RS Alonso, J Prieto, F de La Prieta… - 2021 IEEE …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) represents billions of devices collecting valuable information
through different sensors to be transferred to the Cloud, where it is stored and processed to …
through different sensors to be transferred to the Cloud, where it is stored and processed to …
[HTML][HTML] Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems
Abstract Although Deep Neural Networks (DNN) have become the backbone technology of
several Internet of Things (IoT) applications, their execution in resource-constrained devices …
several Internet of Things (IoT) applications, their execution in resource-constrained devices …
Learning and game based spectrum allocation model for internet of medical things (IoMT) platform
S Kim - IEEE Access, 2023 - ieeexplore.ieee.org
The Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-
home monitoring networks. Nowadays, these services play an imperative part in the life of …
home monitoring networks. Nowadays, these services play an imperative part in the life of …
Imperfect CSI-Based Resource Management in Cognitive IoT Networks: A Deep Recurrent Reinforcement Learning Framework
The proliferation of the Internet of Things (IoT) technology for wide range of wireless
applications increase raw data, leads to spectrum scarcity, and also burdens available …
applications increase raw data, leads to spectrum scarcity, and also burdens available …