[HTML][HTML] Multi-objective optimization of energy saving and throughput in heterogeneous networks using deep reinforcement learning

K Ryu, W Kim - Sensors, 2021 - mdpi.com
Wireless networking using GHz or THz spectra has encouraged mobile service providers to
deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As …

[HTML][HTML] Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

A Mughees, M Tahir, MA Sheikh, A Amphawan… - Physical …, 2023 - Elsevier
Small cells are a promising technique to improve the capacity and throughput of future
wireless networks. However, user association and power allocation in heterogeneous …

Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks

V Mohanavel, M Tamilselvi… - … Conference on I …, 2022 - ieeexplore.ieee.org
Large bandwidth and more mobility are only two reasons why wireless and mobile networks
are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous …

Deep reinforcement learning-assisted energy harvesting wireless networks

J Ye, H Gharavi - IEEE transactions on green communications …, 2020 - ieeexplore.ieee.org
Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a
promising approach to deal with the ever-growing traffic that can severely impact the power …

Deep reinforcement learning for cell on/off energy saving on wireless networks

JS Pujol–Roigl, S Wu, Y Wang… - 2021 IEEE Global …, 2021 - ieeexplore.ieee.org
Increased network traffic demands have led to ex-tremely dense network deployments. This
translates to significant growth in energy consumption at the radio access networks …

Deep reinforcement learning resource allocation in wireless sensor networks with energy harvesting and relay

B Zhao, X Zhao - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Green wireless communications have been extensively studied in wireless sensor networks
(WSNs), including the use of new energy, renewable energy, and low-power consumption …

Deep reinforcement learning method for energy efficient resource allocation in next generation wireless networks

Z Zhang, H Qu, J Zhao, W Wang - … on computing, networks and internet of …, 2020 - dl.acm.org
The next generation wireless networks (NGWNs) of a base station (BS) with ultra-high dense
user equipment's deployment are studied. To extend the coverage area and increase the …

[HTML][HTML] Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning

CK Hsieh, KL Chan, FT Chien - Applied Sciences, 2021 - mdpi.com
This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …

Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks

A Mohajer, F Sorouri, A Mirzaei, A Ziaeddini… - IEEE Systems …, 2022 - ieeexplore.ieee.org
The dense deployment of small-cell networks is a key feature of the next-generation mobile
networks employed to provide the necessary capacity increase. The small cells are installed …

A survey on applications of deep reinforcement learning in resource management for 5G heterogeneous networks

YL Lee, D Qin - 2019 Asia-Pacific Signal and Information …, 2019 - ieeexplore.ieee.org
Heterogeneous networks (HetNets) have been regarded as the key technology for fifth
generation (5G) communications to support the explosive growth of mobile traffics. By …