Machine Learning in 5G Wireless Networks

[1]  Marco Pavone,et al.  Cellular Network Traffic Scheduling With Deep Reinforcement Learning , 2018, AAAI.

[2]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[3]  Fabio Martinelli,et al.  Evaluating Convolutional Neural Network for Effective Mobile Malware Detection , 2017, KES.

[4]  Adam Doupé,et al.  Deep Android Malware Detection , 2017, CODASPY.

[5]  Chih-Wei Huang,et al.  A study of deep learning networks on mobile traffic forecasting , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[6]  Abdelkader Outtagarts,et al.  Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking , 2018, QSHINE.

[7]  Paul Patras,et al.  Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks , 2017, MobiHoc.

[8]  Xiang Cheng,et al.  Exploiting Mobile Big Data: Sources, Features, and Applications , 2017, IEEE Network.

[9]  Shekhar Verma,et al.  Graph Laplacian Regularization With Procrustes Analysis for Sensor Node Localization , 2017, IEEE Sensors Journal.

[10]  Zhu Han,et al.  Joint User Scheduling and Content Caching Strategy for Mobile Edge Networks Using Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[11]  Jianxin Wu,et al.  Minimal gated unit for recurrent neural networks , 2016, International Journal of Automation and Computing.

[12]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  Yangmin Lee,et al.  Classification of node degree based on deep learning and routing method applied for virtual route assignment , 2017, Ad Hoc Networks.

[15]  Antonio Pescapè,et al.  Mobile Encrypted Traffic Classification Using Deep Learning , 2018, 2018 Network Traffic Measurement and Analysis Conference (TMA).

[16]  Robert Piché,et al.  A Survey of Selected Indoor Positioning Methods for Smartphones , 2017, IEEE Communications Surveys & Tutorials.

[17]  Shui Yu,et al.  Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks , 2018, Wirel. Commun. Mob. Comput..

[18]  Kwangjo Kim,et al.  Detecting Impersonation Attack in WiFi Networks Using Deep Learning Approach , 2016, WISA.

[19]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[20]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[21]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[22]  Mustafa ElNainay,et al.  CNN based Indoor Localization using RSS Time-Series , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[23]  Chadi Assi,et al.  Deep reinforcement learning-based scheduling for roadside communication networks , 2017, 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[24]  LloretJaime,et al.  Distributed flood attack detection mechanism using artificial neural network in wireless mesh networks , 2016 .

[25]  Yi Liu,et al.  Indoor Fingerprint Positioning Based on Wi-Fi: An Overview , 2017, ISPRS Int. J. Geo Inf..

[26]  Georg Carle,et al.  Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning , 2018, Big-DAMA@SIGCOMM.

[27]  Mohsen Guizani,et al.  Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services , 2018, IEEE Internet of Things Journal.

[28]  José Manuel Andújar Márquez,et al.  A New Metre for Cheap, Quick, Reliable and Simple Thermal Transmittance (U-Value) Measurements in Buildings , 2017, Sensors.

[29]  Alex Pentland,et al.  Using Deep Learning to Predict Demographics from Mobile Phone Metadata , 2015, ArXiv.

[30]  Giuseppe Aceto,et al.  Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges , 2019, IEEE Transactions on Network and Service Management.

[31]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[32]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[33]  Jaime Lloret,et al.  Distributed flood attack detection mechanism using artificial neural network in wireless mesh networks , 2016, Secur. Commun. Networks.

[34]  Jason Jianjun Gu,et al.  Deep Neural Networks for wireless localization in indoor and outdoor environments , 2016, Neurocomputing.

[35]  Jaime Lloret,et al.  Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT , 2017, Sensors.

[36]  Marianne Winslett,et al.  Mercury: Metro density prediction with recurrent neural network on streaming CDR data , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[37]  Xiao Zhang,et al.  Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach , 2017, IEEE Transactions on Vehicular Technology.

[38]  Soung Chang Liew,et al.  Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks , 2019, IEEE J. Sel. Areas Commun..

[39]  Xinlei Chen,et al.  DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction , 2018, IEEE Network.

[40]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[41]  Daqing Zhang,et al.  Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization , 2018, J. Netw. Comput. Appl..

[42]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[43]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[44]  Jing Wang,et al.  Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[45]  H. T. Kung,et al.  Inferring Origin Flow Patterns in Wi-Fi with Deep Learning , 2014, ICAC.

[46]  Mahdi Jafari Siavoshani,et al.  Deep packet: a novel approach for encrypted traffic classification using deep learning , 2017, Soft Computing.

[47]  Shuo Liu,et al.  Dynamic Spectrum Assignment for Land Mobile Radio with Deep Recurrent Neural Networks , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[48]  Richard E. Overill,et al.  Detection of known and unknown DDoS attacks using Artificial Neural Networks , 2016, Neurocomputing.

[49]  Steven Latré,et al.  A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[50]  Zhisheng Niu,et al.  DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning , 2018, IEEE Internet of Things Journal.

[51]  Nei Kato,et al.  Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning , 2017, IEEE Transactions on Computers.

[52]  Kobi Cohen,et al.  Deep Multi-User Reinforcement Learning for Dynamic Spectrum Access in Multichannel Wireless Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[53]  Adlen Ksentini,et al.  Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach , 2018, IEEE Network.

[54]  Ming Zhu,et al.  End-to-end encrypted traffic classification with one-dimensional convolution neural networks , 2017, 2017 IEEE International Conference on Intelligence and Security Informatics (ISI).

[55]  Lionel M. Ni,et al.  A Survey on Wireless Indoor Localization from the Device Perspective , 2016, ACM Comput. Surv..

[56]  Laura Pierucci,et al.  A Neural Network for Quality of Experience Estimation in Mobile Communications , 2016, IEEE MultiMedia.

[57]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[58]  Fathi M. Salem,et al.  Simplified minimal gated unit variations for recurrent neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[59]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[60]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[61]  Piet Demeester,et al.  Distributed Neural Networks for Internet of Things: The Big-Little Approach , 2015, IoT 360.

[62]  Nadra Guizani,et al.  Recent Advances and Challenges in Mobile Big Data , 2018, IEEE Communications Magazine.