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- Title
Machine learning and deep learning methods for wireless network applications.
- Authors
Chen, Abel C. H.; Jia, Wen-Kang; Hwang, Feng-Jang; Liu, Genggeng; Song, Fangying; Pu, Lianrong
- Abstract
Wireless networks have been widely adopted and introduced in the areas of engineering, manufacturing, weather monitoring, transportation, etc., to collect data to improve the quality of decision making, but issues arise, such as large volumes of data, incomplete and incompatible data sets, and noise data that prevent from realizing the true value and exploiting their full potentials. Machine learning and deep learning methods have been used as powerful tools to perform feature detection/extraction and trend estimation/forecasting in wireless networks applications. Furthermore, the reinforcement learning methods, including generative adversarial networks (GANs) and deep Q-networks (DQNs) [[6]], are tools for generative networks and discriminative networks to optimize the contesting process in a zero-sum game framework. Topics covered in this issue are categorized into the following six themes: (1) networking optimization, (2) unmanned aerial vehicle (UAV) applications, (3) wireless sensor networks, (4) network security, (5) mobile positioning, and (6) image-based applications.
- Subjects
DEEP learning; MACHINE learning; REINFORCEMENT learning; SUPERVISED learning; WIRELESS sensor networks; NAIVE Bayes classification
- Publication
EURASIP Journal on Wireless Communications & Networking, 2022, Vol 2022, Issue 1, p1
- ISSN
1687-1472
- Publication type
Article
- DOI
10.1186/s13638-022-02196-2