Throughput maximization by deep reinforcement learning with energy cooperation for renewable ultradense IoT networks

Y Li, X Zhao, H Liang - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
Ultradense network (UDN) is considered as one of the key technologies for the explosive
growth of mobile traffic demand on the Internet of Things (IoT). It enhances network capacity …

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 based dynamic resource allocation in 5G ultra-dense networks

Z Liu, X Chen, Y Chen, Z Li - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The rapid development of Internet of things (IoT) technology has promoted the densification
of network infrastructure. Ultra-dense networks (UDN) will become a key technology in 5G …

Deep Q-learning based dynamic resource allocation for self-powered ultra-dense networks

H Li, H Gao, T Lv, Y Lu - 2018 IEEE International Conference …, 2018 - ieeexplore.ieee.org
Though enhancing the capacity and coverage of cellular networks to meet the explosive
increasing of traffic demands, Ultra-Dense Network (UDN) suffers from great power …

Deep deterministic policy gradient based dynamic power control for self-powered ultra-dense networks

H Li, T Lv, X Zhang - 2018 IEEE Globecom Workshops (GC …, 2018 - ieeexplore.ieee.org
By densely deploying the base stations (BSs), Ultra Dense Network (UDN) exhibits strong
potential to enhance the network capacity, while leading to huge power consumption and a …

Energy-efficient ultra-dense network with deep reinforcement learning

H Ju, S Kim, Y Kim, B Shim - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large
number of small cells are densely deployed on top of macro cells has received a great deal …

Traffic offloading and power allocation for green HetNets using reinforcement learning method

B Gu, Y Wei, X Liu, M Song… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
In order to satisfy the boosting mobile traffic demand, the deployment of small cells has been
regarded as a feasible solution. But the growth of network infrastructure leads to a …

Energy efficient clustering and resource allocation strategy for ultra-dense networks: A machine learning framework

N Sharma, K Kumar - IEEE Transactions on Network and …, 2022 - ieeexplore.ieee.org
The ultra-dense network structure of 5G with dense femto-cells deployment, is identified as a
prospective way out for the problem of growing demand for cellular services. However …

Backscatter-assisted computation offloading for energy harvesting IoT devices via policy-based deep reinforcement learning

Y Xie, Z Xu, Y Zhong, J Xu, S Gong… - 2019 IEEE/CIC …, 2019 - ieeexplore.ieee.org
Wireless Internet of Things (IoT) devices can be deployed for data acquisition and decision
making, eg, the wearable sensors used for healthcare monitoring. Due to limited …

Dynamic power allocation in IIoT based on multi-agent deep reinforcement learning

F Li, Z Liu, X Zhang, Y Yang - Neurocomputing, 2022 - Elsevier
With the rapidly growing fifth generation (5G) wireless data traffic, the cellular network has
gradually become an important mode for the Industrial Internet of Things (IIoT). To give full …