Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
A GNN-based supervised learning framework for resource allocation in wireless IoT networks
The Internet of Things (IoT) allows physical devices to be connected over the wireless
networks. Although device-to-device (D2D) communication has emerged as a promising …
networks. Although device-to-device (D2D) communication has emerged as a promising …
Deep learning for intelligent IoT: Opportunities, challenges and solutions
Next-generation wireless networks have to be robust and self-sustained. Internet of things
(IoT) is reshaping the technological adaptation in the daily life of human beings. IoT …
(IoT) is reshaping the technological adaptation in the daily life of human beings. IoT …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
Intelligent traffic adaptive resource allocation for edge computing-based 5G networks
The popularity of smart mobile devices has led to a tremendous increase in mobile traffic,
which has put a considerable strain on the fifth generation of mobile communication …
which has put a considerable strain on the fifth generation of mobile communication …
Resource allocation in uplink NOMA-IoT networks: A reinforcement-learning approach
Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to
enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication …
enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Intelligent-driven green resource allocation for industrial Internet of Things in 5G heterogeneous networks
P Yu, M Yang, A Xiong, Y Ding, W Li… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) is one of the important applications under the 5G
massive machine type of communication (mMTC) scenario. To ensure the high reliability of …
massive machine type of communication (mMTC) scenario. To ensure the high reliability of …
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