Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networks
L Deng, D Wu, Z Liu, Y Zhang, YS Han - arXiv preprint arXiv:2205.02057, 2022 - arxiv.org
In this paper, we for the first time investigate the random access problem for a delay-
constrained heterogeneous wireless network. We begin with a simple two-device problem …
constrained heterogeneous wireless network. We begin with a simple two-device problem …
Reinforcement learning random access for delay-constrained heterogeneous wireless networks: A two-user case
D Wu, L Deng, Z Liu, Y Zhang… - 2021 IEEE Globecom …, 2021 - ieeexplore.ieee.org
In this paper, we investigate the random access problem for a delay-constrained
heterogeneous wireless network. As a first attempt to study this new problem, we consider a …
heterogeneous wireless network. As a first attempt to study this new problem, we consider a …
Throughput optimization for grant-free multiple access with multiagent deep reinforcement learning
Grant-free multiple access (GFMA) is a promising paradigm to efficiently support uplink
access of Internet of Things (IoT) devices. In this paper, we propose a deep reinforcement …
access of Internet of Things (IoT) devices. In this paper, we propose a deep reinforcement …
Meta Reinforcement Learning for Generalized Multiple Access in Heterogeneous Wireless Networks
This paper focuses on spectrum sharing in heterogenous wireless networks, where different
nodes utilize various Media Access Control (MAC) protocols to transmit data packets to a …
nodes utilize various Media Access Control (MAC) protocols to transmit data packets to a …
Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
YZ Bekele, YJ Choi - Sensors, 2021 - mdpi.com
5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as
the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity …
the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity …
The Story of : ALOHA-based and Reinforcement-Learning-based Random Access for Delay-Constrained Communications
Motivated by the proliferation of real-time applications in multimedia communication
systems, tactile Internet, and cyber-physical systems, supporting delay-constrained traffic …
systems, tactile Internet, and cyber-physical systems, supporting delay-constrained traffic …
Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning
Y Xu, J Lou, T Wang, J Shi, T Zhang, A Paul, Z Wu - Electronics, 2023 - mdpi.com
In heterogeneous wireless networks, when multiple nodes need to share the same wireless
channel, they face the issue of multiple access, which necessitates a Medium Access …
channel, they face the issue of multiple access, which necessitates a Medium Access …
Deep reinforcement learning-based dynamic multichannel access for heterogeneous wireless networks with DenseNet
K Zong - 2021 IEEE/CIC International Conference on …, 2021 - ieeexplore.ieee.org
In this paper, we consider the problem of dynamic multi-channel access in the
heterogeneous wireless networks, where multiple independent channels are shared by …
heterogeneous wireless networks, where multiple independent channels are shared by …
Carrier-sense multiple access for heterogeneous wireless networks using deep reinforcement learning
This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that
employ deep reinforcement learning (DRL) techniques for heterogeneous wireless …
employ deep reinforcement learning (DRL) techniques for heterogeneous wireless …
Intelligent Decentralized Multiple Access via Multi-Agent Deep Reinforcement Learning
Y He, X Gang, Y Gao - 2024 IEEE Wireless Communications …, 2024 - ieeexplore.ieee.org
In this paper, we propose a multi-agent proximal policy optimization (MAPPO) based
algorithm, PPO-DMA, with centralized training and decentralized execution (CTDE) …
algorithm, PPO-DMA, with centralized training and decentralized execution (CTDE) …