Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
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 …
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 …
Multi-agent reinforcement learning-based distributed dynamic spectrum access
Dynamic spectrum access (DSA) is an effective solution for efficiently utilizing the radio
spectrum by sharing it among various networks. Two primary tasks of a DSA controller are …
spectrum by sharing it among various networks. Two primary tasks of a DSA controller are …
Deep residual learning-based cognitive model for detection and classification of transmitted signal patterns in 5G smart city networks
Primary user (PU) signal detection or classification is a critical component of cognitive radio
(CR) related wireless communication applications. In CR, the PU detection methods are …
(CR) related wireless communication applications. In CR, the PU detection methods are …
Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks
The main challenge with Vehicular Ad-Hoc Networks (VANETs) for assisting Intelligent
Transportation Services (ITSs) is ensuring effective data delivery under various network …
Transportation Services (ITSs) is ensuring effective data delivery under various network …
A Review of Research on Spectrum Sensing Based on Deep Learning
Y Zhang, Z Luo - Electronics, 2023 - mdpi.com
In recent years, with the rapid development in wireless communication and 5G networks, the
rapid growth in mobile users has been accompanied by an increasing demand for the …
rapid growth in mobile users has been accompanied by an increasing demand for the …
Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems
Cognitive radio (CR) provides an effective solution to meet the huge bandwidth
requirements in intelligent transportation systems (ITS), which enables secondary users …
requirements in intelligent transportation systems (ITS), which enables secondary users …
Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network
MK Giri, S Majumder - Physical Communication, 2023 - Elsevier
This paper addresses the problem of distributed dynamic spectrum access in a cognitive
radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the …
radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the …
[HTML][HTML] Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review
Smart transportation systems implemented through vehicular ad hoc networks (VANET) offer
significant potential to improve safety. However, the network faces critical challenges related …
significant potential to improve safety. However, the network faces critical challenges related …