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EditorialDeep Learning Driven Wireless Communications andMobile Computing
Huaming Wu ,1 Zhu Han,2 Katinka Wolter,3 Yubin Zhao,4 and Haneul Ko 5
1Tianjin University, Tianjin 300072, China2University of Houston, Houston, TX 77004, USA3Freie Universitat Berlin, Berlin 14195, Germany4Chinese Academy of Sciences, Shenzhen 518055, China5Korea University, Sejong 30019, Republic of Korea
Correspondence should be addressed to Huaming Wu; [email protected]
Received 28 March 2019; Accepted 28 March 2019; Published 28 April 2019
Copyright © 2019 Huaming Wu et al. �is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the exploding amount of mobile traffic data andunprecedented demands of computing, executing the everincreasingly complex applications in resource-constrainedmobile devices becomes more and more challenging. Futurewireless communications will be very complex with hetero-geneous radio access technologies, transmission links, andnetwork slices. More intelligent technologies are required toaddress those complex scenarios and to adapt to dynamicmobile environments. Recently, deep learning has attractedmuch attention in the field of wireless communication andmobile computing. Deep learning driven algorithms andmodels can facilitate wireless network analysis and resourcemanagement, benefit in coping with the growth in volumesof communication and computation for emerging mobileapplications. However, how to customize deep learningtechniques for heterogeneous mobile environments is stillunder development. Learning algorithms in mobile wirelesssystems are immature and inefficient. In this special issueon deep learning driven wireless communication and mobilecomputing, we have accepted seven papers that include boththeoretical contributions and practical research related to thenew technologies, analysis, and applications with the help ofartificial intelligence and deep learning.
One paper proposes a new fault detection mechanismbased on support vector regression among sensor observa-tions in wireless sensor networks according to the redundant
information of meteorological elements collected by multi-sensors.�e proposed algorithm reduces the communicationto sensor nodes and further achieves a high detection accu-racy and a low false alarm ratio, which are more suitable forfault detection inmeteorological sensor networks, even whenthe failure rate is very high.
Another paper presents a survey on deep learning tech-niques in signal recognition. Classical methods, emergingmachine learning, and deep leaning schemes are extendedfrom modulation recognition to wireless technology recog-nition. �is survey discusses the open problems, new chal-lenges, and future development trends on signal recognitionin practical applications, e.g., burst signal recognition andunknown signal recognition.
One of the papers addresses the traffic explosion problemin hierarchical wireless networks. It proposes a Double DeepQ-network (Double DQN) based edge caching strategy, inorder to maximize unloading traffic and reduce pressurethrough D2D communication. �e proposed cache replace-ment strategy is established using a Markov decision process(MDP) and a deep reinforcement learning framework.
Another paper presents an integrated method combininga deep neural network (DNN) with an improved K-NearestNeighbor (KNN) algorithm to enhance the accuracy ofindoor positioning. �e DNN algorithm is first used toclassify the WiFi RSSI Fingerprinting dataset, and then
HindawiWireless Communications and Mobile ComputingVolume 2019, Article ID 4578685, 2 pageshttps://doi.org/10.1155/2019/4578685
2 Wireless Communications and Mobile Computing
these possible locations are classified by the improved KNNalgorithm to determine the final position.
Another paper aims to understand the intention of legalconsultation of users with different language expressionsand legal knowledge background. It combines deep neuralnetwork Bidirectional Long Short-TermMemory (Bi-LSTM)with pattern-oriented tensor decomposition to perform theclassification and deep understanding of a users’ legal con-sulting statements. Experiments show that the proposedmethod is more instructive and interpretable, and it alsodemonstrates faster convergence and higher accuracy thanthe traditional deep neural networks.
One of the papers addresses the issue of how to ranknodes frompositive and negative views of social network datamining. It develops a random walk based method for signednetworks that allows the walker to change between positiveand negative while crossing from one node to another.
Another paper proposes a deep Convolutional Neu-ral Network (CNN) assisted personalized recommenda-tion framework for mobile wireless networks. It combinesthe mobile user's location trajectory sequence, user-sharedimages, and text information and recommends potentialvisiting locations to users. �is is achieved by using the bigdata sampled as the user’s social and mobile trajectory andprocessing it through the CNN network.
We have been impressed with the variety of topicssubmitted to this special issue and we hope the reader willenjoy the papers as much as we did. We also hope that thedeep learning driven algorithms and models demonstratedin this special issue will be helpful to develop customizeddeep learning techniques for heterogeneous wireless networkarchitectures, mobile applications, and mobile systems.
Conflicts of Interest
As guest editors, we have no conflicts of interest to theaccepted papers.
Acknowledgments
We thank all authors for their submission to this special issueand we thank all reviewers for their high-quality feedback.
Huaming WuZhu Han
Katinka WolterYubin ZhaoHaneul Ko
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