Dynamic spectrum access for Internet-of-Things based on federated deep reinforcement learning
The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communicati...
Saved in:
Main Authors: | Li, Feng, Shen, Bowen, Guo, Jiale, Lam, Kwok-Yan, Wei, Guiyi, Wang, Li |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/163822 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning
by: Zhang, Songbo, et al.
Published: (2023) -
Trading-based dynamic spectrum access and allocation in cognitive internet of things
by: Li, Feng, et al.
Published: (2020) -
Local differential privacy-based federated learning for Internet of Things
by: Zhao, Yang, et al.
Published: (2021) -
Deep-reinforcement-learning-based energy-efficient resource management for social and cognitive Internet of Things
by: Yang, Helin, et al.
Published: (2020) -
Internet of things in the Philippines: A review
by: Illahi, Ana Antoniette C., et al.
Published: (2019)