Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues
In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …
host to observe, learn, and respond to its complex and dynamic operating environment in an …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
A deep actor-critic reinforcement learning framework for dynamic multichannel access
To make efficient use of limited spectral resources, we in this work propose a deep actor-
critic reinforcement learning based framework for dynamic multichannel access. We …
critic reinforcement learning based framework for dynamic multichannel access. We …
[图书][B] Cognitive radio communication and networking: Principles and practice
The author presents a unified treatment of this highly interdisciplinary topic to help define the
notion of cognitive radio. The book begins with addressing issues such as the fundamental …
notion of cognitive radio. The book begins with addressing issues such as the fundamental …
BENS− B5G: blockchain-enabled network slicing in 5G and beyond-5G (B5G) networks
Fifth-generation (5G) technology is anticipated to allow a slew of novel applications across a
variety of industries. The wireless communication of the 5G and Beyond-5G (B5G) networks …
variety of industries. The wireless communication of the 5G and Beyond-5G (B5G) networks …
Application of reinforcement learning in cognitive radio networks: Models and algorithms
Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in
licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning …
licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning …
LSTM-based channel access scheme for vehicles in cognitive vehicular networks with multi-agent settings
TD Le, G Kaddoum - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
In this paper, we study the channel access problem of vehicles in a cognitive radio vehicular
network, where each vehicle opportunistically accesses the channel resources of the …
network, where each vehicle opportunistically accesses the channel resources of the …
A survey on dynamic spectrum access techniques in cognitive radio networks
The idea of Cognitive Radio (CR) is to share the spectrum between a user called primary,
and a user called secondary. Dynamic Spectrum Access (DSA) is a new spectrum sharing …
and a user called secondary. Dynamic Spectrum Access (DSA) is a new spectrum sharing …
Deep-reinforcement learning for fair distributed dynamic spectrum access in wireless networks
SB Janiar, V Pourahmadi - 2021 IEEE 18th Annual Consumer …, 2021 - ieeexplore.ieee.org
Many studies investigated fair dynamic spectrum access in distributed wireless networks
(DWNs). To limit the required communication between nodes, several schemes based on …
(DWNs). To limit the required communication between nodes, several schemes based on …
Cooperative channel assignment for VANETs based on multiagent reinforcement learning
Y Wang, K Zheng, D Tian, X Duan, J Zhou - Frontiers of Information …, 2020 - Springer
Dynamic channel assignment (DCA) plays a key role in extending vehicular ad-hoc network
capacity and mitigating congestion. However, channel assignment under vehicular direct …
capacity and mitigating congestion. However, channel assignment under vehicular direct …