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Call for papers: Machine Learning and Deep Learning Methods for Wireless Network Applications

EURASIP Journal on Wireless Communications and Networking welcomes submissions to the special issue on 'Machine Learning and Deep Learning Methods for Wireless Network Applications'.

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.

Topics of interest include but aren't limited to:

  • New Supervised Machine Learning Methods for Wireless Network Applications
  • New of Unsupervised Machine Learning Methods for Wireless Network Applications
  • Novel Reinforcement Learning Methods for Wireless Network Applications
  • New Optimization Methods for Machine Learning and the applications in Wireless Network

Submission Instructions

Before submitting your manuscript, please ensure you have carefully read the submission guidelines for  EURASIP Journal on Wireless Communications and Networking. The complete manuscript should be submitted through the  EURASIP Journal on Wireless Communications and Networking submission system. To ensure that you submit to the correct special issue please select the appropriate special issue in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Machine Learning and Deep Learning Methods for Wireless Network Applications'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Deadline for submissions: 

31st December 2019

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

Submissions will also benefit from the usual advantages of open access publication:

  • Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
  • High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
  • No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
  • Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed

For editorial enquiries please contact vincent.salvo@springer.com. 

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