A deep reinforcement learning for user association and power control in heterogeneous networks

H Ding, F Zhao, J Tian, D Li, H Zhang - Ad Hoc Networks, 2020 - Elsevier
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …

[HTML][HTML] Deep reinforcement learning-based adaptive modulation for underwater acoustic communication with outdated channel state information

Y Zhang, J Zhu, H Wang, X Shen, B Wang, Y Dong - Remote Sensing, 2022 - mdpi.com
Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel
state information (CSI) but the long propagation delays and time-varying features of UWA …

Power and rate control in wireless communication systems with energy harvesting and rateless codes

M Liu, W Lei, J Sun, H Lei, H Tang - Physical Communication, 2023 - Elsevier
In this paper, for a wireless communication system with energy harvesting and rateless error
correction codes, the joint optimization of transmit power, modulation scheme and code rate …

Learning-based joint optimization of transmit power and harvesting time in wireless-powered networks with co-channel interference

K Lee, JR Lee, HH Choi - IEEE Transactions on Vehicular …, 2020 - ieeexplore.ieee.org
In this paper, we consider a wireless-powered network with co-channel interference where
the transmitters control their transmit power and receivers harvest wireless energy using a …

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 …

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 …

Transmitter-oriented dual-mode SWIPT with deep-learning-based adaptive mode switching for IoT sensor networks

JJ Park, JH Moon, KY Lee… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
In this article, we propose a dual-mode simultaneous wireless information and power
transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) …

Novel adaptive transmission scheme for effective URLLC support in 5G NR: A model-based reinforcement learning solution

NS Saatchi, HC Yang, YC Liang - IEEE Wireless …, 2022 - ieeexplore.ieee.org
Future industrial Internet of Things (IIoT) applications demand trustworthy ultra-reliable and
low-latency communications (URLLC) service. In this letter, we jointly design available …

[HTML][HTML] Energy Efficient Power Allocation in Massive MIMO Based on Parameterized Deep DQN

S Sharma, W Yoon - Electronics, 2023 - mdpi.com
Machine learning offers advanced tools for efficient management of radio resources in
modern wireless networks. In this study, we leverage a multi-agent deep reinforcement …

Hypergraph-Based Interference Avoidance Resource Management in Customer-Centric Communication for Intelligent Cyber-physical Transportation Systems

J Huang, S Zhang, F Yang, T Yu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
In customer-centric communication for intelligent cyber-physical transportation systems
(ICTS), the extensive deployment of customer electronics will lead to massive overlapping …