Actor-critic deep learning for efficient user association and bandwidth allocation in dense mobile networks with green base stations
QV Do, I Koo - Wireless Networks, 2019 - Springer
In this paper, we introduce an efficient user-association and bandwidth-allocation scheme
based on an actor-critic deep learning framework for downlink data transmission in dense …
based on an actor-critic deep learning framework for downlink data transmission in dense …
Improving performance of IEEE 802.11 by a dynamic control backoff algorithm under unsaturated traffic loads
The IEEE 802.11 backoff algorithm is very important for controlling system throughput over
contentionbased wireless networks. For this reason, there are many studies on wireless …
contentionbased wireless networks. For this reason, there are many studies on wireless …
Efficient Communications for Multi-Agent Reinforcement Learning in Wireless Networks
Multi-agent reinforcement learning (RL) utilizes the observations and learning experiences
shared among the agents to accelerate learning speed under partial observations and the …
shared among the agents to accelerate learning speed under partial observations and the …
Study of contention window adjustment for csma/ca by using machine learning
YW Chen, KC Kao - … 22nd Asia-Pacific Network Operations and …, 2021 - ieeexplore.ieee.org
In IEEE 802.11, CSMA/CA protocol applies the exponential backoff scheme to relax the
contention problems among different clients wishing to transmit data at the same time. A …
contention problems among different clients wishing to transmit data at the same time. A …
Multi-Agent Reinforcement Learning based Uplink OFDMA for IEEE 802.11 ax Networks
In the IEEE 802.11 ax Wireless Local Area Networks (WLANs), Orthogonal Frequency
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN …
Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning
Wireless resource utilizations are the focus of future communication, which are used
constantly to alleviate the communication quality problem caused by the explosive …
constantly to alleviate the communication quality problem caused by the explosive …
Reliability optimization for channel resource allocation in multihop wireless network: A multigranularity deep reinforcement learning approach
Y Wang, F Shang, J Lei - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
This article investigates the high-reliable data transmission for multihop and multichannel
wireless sensor networks (WSNs), which jointly optimizes the channel allocation and …
wireless sensor networks (WSNs), which jointly optimizes the channel allocation and …
Deep-reinforcement-learning-based drone base station deployment for wireless communication services
GB Tarekegn, RT Juang, HP Lin… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Over the last few years, drone base station (DBS) technology has been recognized as a
promising solution to the problem of network design for wireless communication systems …
promising solution to the problem of network design for wireless communication systems …
An exponential-linear backoff algorithm for contention-based wireless networks
In this paper, a backoff mechanism, Exponential Linear Backoff Algorithm (ELBA), is
proposed to improve system performance over contention-based wireless networks. In the …
proposed to improve system performance over contention-based wireless networks. In the …
An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network
P Khuntia, R Hazra - Transactions on Emerging …, 2019 - Wiley Online Library
In this paper, a novel Deep Q‐learning in addition with an extended Kalman filter (EKF) is
proposed to solve the channel and power allocation issue for a device‐to‐device enabled …
proposed to solve the channel and power allocation issue for a device‐to‐device enabled …