Manuscript Due

Dec. 31, 2019 (in 9 months, 1 week)


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 etc. 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. Supervised machine learning methods, for example, neural network (NN), convolutional neural network (CNN), and recurrent neural network (RNN) can be exploited in the applications pertinent to prediction and classification, whereas unsupervised machine learning methods such as restricted Boltzmann machine (RBM), deep belief network (DBN), deep Boltzmann machine (DBM), auto-encoder (AE), and denoising auto-encoder (DAE) can be utilized for data denoising and model generalization. Furthermore, the reinforcement learning methods, including generative adversarial networks (GANs) and deep Q-networks (DQNs), are tools for generative networks and discriminative networks to optimize the contesting process in a zero-sum game framework. These well-developed methods can contribute substantially, to better improve predictions and classifications in the relevant applications, but there are some issues and limitations that require further attention from the research communities.

Lead Guest Editor

  • Chi-Hua Chen, Fuzhou University, China

Guest Editors

  • Wen-Kang Jia, Fujian Normal University, China
  • Feng-Jang Hwang, University of Technology Sydney, Australia
  • Genggeng Liu, Fuzhou University, China
  • Fangying Song, Fuzhou University, China
  • Lianrong Pu, Fuzhou University, China
EURASIP Journal on Wireless Communications and Networking