Deep reinforcement learning approach to QoE-driven resource allocation for spectrum underlay in cognitive radio networks
F Shah-Mohammadi… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
This paper presents a deep reinforcement learning-based technique for cognitive radio
underlay dynamic spectrum access (DSA) that performs distributed joint multi-resource …
underlay dynamic spectrum access (DSA) that performs distributed joint multi-resource …
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 …
Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning
DH Tashman, S Cherkaoui… - … and Mobile Computing …, 2023 - ieeexplore.ieee.org
In this paper, a reinforcement learning technique is employed to maximize the performance
of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed …
of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed …
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 …
Deep learning-inspired message passing algorithm for efficient resource allocation in cognitive radio networks
M Liu, T Song, J Hu, J Yang… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on
optimizing the network performance in cognitive radio networks. In this paper, an EE+ SE …
optimizing the network performance in cognitive radio networks. In this paper, an EE+ SE …
Deep reinforcement learning-based optimization for irs-assisted cognitive radio systems
In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio
system and maximize the secondary user (SU) rate by jointly optimizing the transmit power …
system and maximize the secondary user (SU) rate by jointly optimizing the transmit power …
A kind of joint routing and resource allocation scheme based on prioritized memories-deep Q network for cognitive radio ad hoc networks
Y Du, F Zhang, L Xue - Sensors, 2018 - mdpi.com
Cognitive Radio (CR) is a promising technology to overcome spectrum scarcity, which
currently faces lots of unsolved problems. One of the critical challenges for setting up such …
currently faces lots of unsolved problems. One of the critical challenges for setting up such …
Optimal Channel Selection and Switching Using Q-Learning in Cognitive Radio Ad Hoc Networks
With the rising demand for spectrum and the emergence of advanced communication
systems, there is a critical requirement for more efficient and streamlined approaches to …
systems, there is a critical requirement for more efficient and streamlined approaches to …
Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
Underlay Cognitive Radio (CR) systems were introduced to resolve the issue of spectrum
scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter …
scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter …