Machine learning-based resource allocation in satellite networks supporting internet of remote things
Satellite networks have been regarded as a promising architecture for supporting the
Internet of remote things (IoRT) due to their advantages of wide coverage and high …
Internet of remote things (IoRT) due to their advantages of wide coverage and high …
A deep reinforcement learning based approach for energy-efficient channel allocation in satellite Internet of Things
B Zhao, J Liu, Z Wei, I You - IEEE Access, 2020 - ieeexplore.ieee.org
Recently, Satellite Internet of Things (SIoT), a space network that consists of numerous Low
Earth Orbit (LEO) satellites, is regarded as a promising technique since it is the only solution …
Earth Orbit (LEO) satellites, is regarded as a promising technique since it is the only solution …
Learning-based computation offloading for IoRT through Ka/Q-band satellite–terrestrial integrated networks
In this article, we propose a multilayer Ka/Q-band satellite–terrestrial integrated network for
the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication …
the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication …
Joint network control and resource allocation for space-terrestrial integrated network through hierarchal deep actor-critic reinforcement learning
Conventional approaches to network control and resource allocation by allocating dedicated
spectrum resources and separate infrastructure for massive Internet of Things (IoT) network …
spectrum resources and separate infrastructure for massive Internet of Things (IoT) network …
Two-timescale learning-based task offloading for remote IoT in integrated satellite-terrestrial networks
In this article, we propose an integrated satellite–terrestrial network (ISTN) architecture to
support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite …
support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite …
Multi-agent drl for resource allocation and cache design in terrestrial-satellite networks
In the past few years, satellite communications have greatly affected our daily lives, and the
integrated terrestrial-satellite network can combine the advantages of satellite and base …
integrated terrestrial-satellite network can combine the advantages of satellite and base …
Deep dyna-reinforcement learning based on random access control in LEO satellite IoT networks
X Liu, H Zhang, K Long, A Nallanathan… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Random access schemes in satellite Internet-of-Things (IoT) networks are being considered
a key technology of new-type machine-to-machine (M2M) communications. However, the …
a key technology of new-type machine-to-machine (M2M) communications. However, the …
The next generation heterogeneous satellite communication networks: Integration of resource management and deep reinforcement learning
This article proposes an innovative resource management framework for the next generation
heterogeneous satellite networks (HSNs), which can achieve cooperation between …
heterogeneous satellite networks (HSNs), which can achieve cooperation between …
Multi-agent deep reinforcement learning-based flexible satellite payload for mobile terminals
X Hu, X Liao, Z Liu, S Liu, X Ding… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Information dissemination in mobile networks turns out to be a problem when the network is
sparse. Mobile networks begin to establish a separate cluster attributable to the limited …
sparse. Mobile networks begin to establish a separate cluster attributable to the limited …
Deep Q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks
With the development of satellite networks, there is an emerging trend to integrate satellite
networks with terrestrial networks, called satellite-terrestrial networks (STNs). The …
networks with terrestrial networks, called satellite-terrestrial networks (STNs). The …