Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11 ah MAC Layer
The IEEE 802.11 ah standard is introduced to address the growing scale of internet of things
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
(IoT) applications. To reduce contention and enhance energy efficiency in the system, the …
Access and radio resource management for IAB networks using deep reinforcement learning
MM Sande, MC Hlophe, BT Maharaj - IEEE Access, 2021 - ieeexplore.ieee.org
Congestion in dense traffic networks is a prominent obstacle towards realizing the
performance requirements of 5G new radio. Since traditional adaptive traffic signal control …
performance requirements of 5G new radio. Since traditional adaptive traffic signal control …
Joint ddpg and unsupervised learning for channel allocation and power control in centralized wireless cellular networks
M Sun, E Mei, S Wang, Y Jin - Ieee Access, 2023 - ieeexplore.ieee.org
In order to solve the resource allocation problem in scenarios of centralized wireless cellular
communication with multiple cells, users and channels, a novel resource allocation …
communication with multiple cells, users and channels, a novel resource allocation …
Joint Power Allocation and User Fairness Optimization for Reinforcement Learning Over mmWave-NOMA Heterogeneous Networks
In this paper, the problem of joint power allocation and user fairness is investigated for an
mmWave heterogeneous network (HetNet) including hybrid non-orthogonal multiple access …
mmWave heterogeneous network (HetNet) including hybrid non-orthogonal multiple access …
基于深度强化学习的反向散射网络资源分配机制.
江巍， 朱江 - Telecommunication Engineering, 2022 - search.ebscohost.com
为了提升反向散射网络中物联网设备的平均吞吐量, 提出了一种资源分配机制,
构建了用户配对和时隙分配联合优化资源分配模型. 由于该模型直接利用深度强化学习(Deep …
构建了用户配对和时隙分配联合优化资源分配模型. 由于该模型直接利用深度强化学习(Deep …
Joint access point selection and resource allocation in MEC-assisted network: A reinforcement learning based approach
Z Li, C Hu, W Wang, Y Li, G Wei - China Communications, 2022 - ieeexplore.ieee.org
A distributed reinforcement learning (RL) based resource management framework is
proposed for a mobile edge computing (MEC) system with both latency-sensitive and …
proposed for a mobile edge computing (MEC) system with both latency-sensitive and …
Service Function Chaining in LEO Satellite Networks via Multi-Agent Reinforcement Learning
K Doan, M Avgeris, A Leivadeas… - … 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide
range of applications such as disaster response and military operations, among others …
range of applications such as disaster response and military operations, among others …
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks
AI Abubakar, MS Mollel, N Ramzan - arXiv preprint arXiv:2311.14509, 2023 - arxiv.org
The constant surge in the traffic demand on cellular networks has led to continuous
expansion in network capacity in order to accommodate existing and new service demands …
expansion in network capacity in order to accommodate existing and new service demands …