Power control in energy harvesting multiple access system with reinforcement learning
The Internet of Things (IoT) application has a crucial need for long-term and self-sustainable
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
Intelligent resource management using multiagent double deep Q-networks to guarantee strict reliability and low latency in IoT network
With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation
applications and apply stringent high reliability and low latency requirements, closely related …
applications and apply stringent high reliability and low latency requirements, closely related …
Enabling sustainable underwater IoT networks with energy harvesting: a decentralized reinforcement learning approach
In this article, we study an energy sustainable Internet-of-Underwater Things (IoUT) network
with tidal energy harvesting. Specifically, an analytical model is first developed to analyze …
with tidal energy harvesting. Specifically, an analytical model is first developed to analyze …
Multi-agent reinforcement learning based energy efficiency optimization in NB-IoT networks
Y Guo, M Xiang - 2019 IEEE Globecom Workshops (GC …, 2019 - ieeexplore.ieee.org
Based on the existing Evolved Packet System (EPS) architecture, Narrowband Internet of
Things (NB-IoT) has been expected as a promising paradigm to support energy-aware …
Things (NB-IoT) has been expected as a promising paradigm to support energy-aware …
Optimal resource allocation considering non-uniform spatial traffic distribution in ultra-dense networks: A multi-agent reinforcement learning approach
Recently, the demand for small cell base stations (SBSs) has been exploding to
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
A spectrum resource sharing algorithm for IoT networks based on reinforcement learning
Z Shi, X Xie, M Kadoch… - 2020 International Wireless …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) has attracted tremendous interest since it can improve production
efficiency and system intelligence significantly. However, with the explosive growth of …
efficiency and system intelligence significantly. However, with the explosive growth of …
Collaborative machine learning for energy-efficient edge networks in 6G
To fulfill the diversified requirements of the emerging Internet of Everything (IoE)
applications, the future sixth generation (6G) mobile network is envisioned as a …
applications, the future sixth generation (6G) mobile network is envisioned as a …
Energy harvesting aware multi-hop routing policy in distributed iot system based on multi-agent reinforcement learning
Energy harvesting technologies offer a promising solution to sustainably power an ever-
growing number of Internet of Things (IoT) devices. However, due to the weak and transient …
growing number of Internet of Things (IoT) devices. However, due to the weak and transient …
Multi-agent deep reinforcement learning based resource allocation for ultra-reliable low-latency internet of controllable things
Y Xiao, Y Song, J Liu - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
As a promising technology in the 5G era, the artificial intelligence (AI) enabled Internet of
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
Short-term and long-term throughput maximization in mobile wireless-powered internet of things
With the evolution of Internet of Things (IoT), some IoT nodes possess a certain degree of
mobility, and the gains of the corresponding channels vary dramatically, incurring the energy …
mobility, and the gains of the corresponding channels vary dramatically, incurring the energy …