IoT-Enabled Machine Learning for an Algorithmic Spectrum Decision Process

This paper investigates a data centric approach for future regulatory spectrum management (SM). Spectrum sensing data are collected by a spectrum environment awareness system built on a cloud-based service of Internet of Things. The data are used to characterize channel behaviors and establish a sharing predictor model which enables a set of efficient machine learning algorithms for automated spectrum sharing decision making. The performance of the decision process is evaluated, illustrating the feasibility and potential of this novel SM approach.

[1]  Kareem E. Baddour,et al.  Spectrum Occupancy Prediction for Land Mobile Radio Bands Using a Recommender System , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[2]  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).

[3]  Li Li,et al.  A cloud-based spectrum environment awareness system , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[4]  Kareem E. Baddour,et al.  Spectrum sharing opportunities in land mobile radio bands: A data-driven approach , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[5]  Andreas Ziegler,et al.  ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.

[6]  M. Arltová,et al.  Selection of Unit Root Test on the Basis of Time Series Length and Value of AR(1) Parameter , 2016 .

[7]  Bo Gao,et al.  An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research , 2016, IEEE Transactions on Cognitive Communications and Networking.

[8]  Athanasios V. Vasilakos,et al.  A Cloud-Based Architecture for the Internet of Spectrum Devices Over Future Wireless Networks , 2016, IEEE Access.

[9]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[10]  Mort Naraghi-Pour,et al.  A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE , 2016, IEEE Internet of Things Journal.

[11]  Sergey Andreev,et al.  Highly dynamic spectrum management within licensed shared access regulatory framework , 2015, IEEE Communications Magazine.

[12]  Zhao Zhang,et al.  Spectrum prediction and channel selection for sensing-based spectrum sharing scheme using online learning techniques , 2015, 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[13]  Michael R. Souryal,et al.  Real-time centralized spectrum monitoring: Feasibility, architecture, and latency , 2015, 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[14]  Phil Romero,et al.  Fast and Unsupervised Classification of Radio Frequency Data Sets Utilizing Machine Learning Algorithms , 2015 .

[15]  Jeffrey H. Reed,et al.  Spectrum access system for the citizen broadband radio service , 2015, IEEE Communications Magazine.

[16]  Todor Cooklev,et al.  A cloud-based approach to spectrum monitoring , 2015, IEEE Instrumentation & Measurement Magazine.

[17]  Michael R. Souryal,et al.  An overview of the NTIA/NIST spectrum monitoring pilot program , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[18]  Lin Ma,et al.  SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks , 2014, TheScientificWorldJournal.

[19]  Alagan Anpalagan,et al.  Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions , 2013, IEEE Communications Surveys & Tutorials.

[20]  Moshe T. Masonta,et al.  Spectrum Decision in Cognitive Radio Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[21]  Sherali Zeadally,et al.  Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey , 2013, IEEE Communications Surveys & Tutorials.

[22]  Dusit Niyato,et al.  Channel status prediction for cognitive radio networks , 2012, Wirel. Commun. Mob. Comput..

[23]  Dennis Roberson,et al.  Empirical modeling of public safety voice traffic in the land mobile radio band , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[24]  Mingyan Liu,et al.  Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study , 2009, IEEE Transactions on Mobile Computing.

[25]  Hiroshi Harada,et al.  Cognitive wireless cloud : A network concept to handle heterogeneous and spectrum sharing type radio access networks , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[26]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .