Wireless Communications and Mobile Computing

Deep Learning Driven Wireless Communications and Mobile Computing


Publishing date
01 Apr 2019
Status
Published
Submission deadline
16 Nov 2018

Lead Editor

1Tianjin University, Tianjin, China

2University of Houston, Houston, USA

3Freie Universität Berlin, Berlin, Germany

4Chinese Academy of Sciences, Shenzhen, China

5Korea University, Seoul, Republic of Korea


Deep Learning Driven Wireless Communications and Mobile Computing

Description

With the exploding increased mobile traffic data and unprecedented demands of computing capabilities, it is very challenging to execute increasingly complex applications in resource-constrained mobile devices. Future wireless communications are very complex with different radio access technologies, transmission backhauls, and network slices. More intelligent technologies are required to address such complicated scenarios and adapt to dynamic mobile environments.

Deep learning has already demonstrated overwhelming advantages in many areas. Recently, it has attracted much attention in the field of wireless communications and mobile computing. Deep learning driven algorithms and models can facilitate wireless network analysis and resource management and be of benefit in coping with the growth in volumes of communication and computation for emerging mobile applications. However, how to customize deep learning techniques for heterogeneous mobile environments is still under discussion. Learning algorithms in mobile wireless systems are immature and inefficient. More endeavors are needed to bridge the gap between deep learning and wireless communications and mobile computing research. This special issue solicits theoretical contributions and practical research related to the new technologies, analysis, and applications with the help of artificial intelligence and deep learning.

Potential topics include but are not limited to the following:

  • New theories, scenarios, and applications of deep learning in wireless communications and mobile computing
  • Design, development, and application of new deep learning algorithms in wireless communications and mobile computing
  • Deep learning driven wireless sensor networks, cellular networks, Wi-Fi networks, and emerging mobile networks
  • Deep learning driven wireless data analysis and mobility analysis
  • Deep learning driven network security, network control, and network switching
  • Deep learning driven heterogeneous wireless network architectures, mobile applications, and mobile systems
  • Deep learning driven computation offloading and data offloading
  • Deep learning driven mobile cloud computing (MCC), mobile edge computing (MEC), or fog computing (FC)

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 4578685
  • - Editorial

Deep Learning Driven Wireless Communications and Mobile Computing

Huaming Wu | Zhu Han | ... | Haneul Ko
  • Special Issue
  • - Volume 2019
  • - Article ID 6082047
  • - Research Article

Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks

Yu Zheng | Xiaolong Xu | Lianyong Qi
  • Special Issue
  • - Volume 2019
  • - Article ID 4813717
  • - Research Article

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

Cong Wan | Yanhui Fang | ... | Yun Wang
  • Special Issue
  • - Volume 2019
  • - Article ID 2589784
  • - Research Article

Learning Users’ Intention of Legal Consultation through Pattern-Oriented Tensor Decomposition with Bi-LSTM

Xiaoding Guo | Hongli Zhang | ... | Shang Li
  • Special Issue
  • - Volume 2019
  • - Article ID 4283857
  • - Research Article

Combination of DNN and Improved KNN for Indoor Location Fingerprinting

Peng Dai | Yuan Yang | ... | Ruqiang Yan
  • Special Issue
  • - Volume 2019
  • - Article ID 2561069
  • - Research Article

Edge Caching for D2D Enabled Hierarchical Wireless Networks with Deep Reinforcement Learning

Wenkai Li | Chenyang Wang | ... | Jianji Ren
  • Special Issue
  • - Volume 2019
  • - Article ID 5629572
  • - Review Article

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Xiaofan Li | Fangwei Dong | ... | Weibin Guo
  • Special Issue
  • - Volume 2018
  • - Article ID 4349795
  • - Research Article

Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

Yong Cheng | Qiuyue Liu | ... | Tariq Umer
Wireless Communications and Mobile Computing
 Journal metrics
Acceptance rate34%
Submission to final decision85 days
Acceptance to publication43 days
CiteScore1.470
Impact Factor1.396
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