Spatio-Temporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-Aided SVM Algorithms

This paper addresses the problem of spectrum sensing in multi-antenna cognitive radio system using the support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenarios as a multiple state signal detection problem. Next, we propose a novel beamformer-aided feature realization strategy for enhancing the capability of the SVM for signal classification under both single and multiple primary users conditions. Then, we investigate the error correcting output codes-based multi-class SVM algorithms and provide a multiple independent model alternative for solving the multiple state spectrum sensing problem. The performance of the proposed detectors is quantified in terms of probability of detection, probability of false alarm, receiver operating characteristics (ROC), area under ROC curves, and overall classification accuracy. Simulation results show that the proposed detectors are robust to both temporal and joint spatio-temporal detection of spectrum holes in cognitive radio networks.

[1]  Josh Petersen,et al.  Modulation recognition using hierarchical deep neural networks , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[2]  Ekram Hossain,et al.  Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

[3]  Sangarapillai Lambotharan,et al.  Transmit beamforming design for two-dimensional phased-MIMO radar with fully-overlapped subarrays , 2014, 2014 Sensor Signal Processing for Defence (SSPD).

[4]  Vasile Palade,et al.  Class Imbalance Learning Methods for Support Vector Machines , 2013 .

[5]  Dong-Ho Cho,et al.  Deep Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks , 2017, ArXiv.

[6]  T. Charles Clancy,et al.  Convolutional Radio Modulation Recognition Networks , 2016, EANN.

[7]  Jin Wei,et al.  Deep learning-based automated modulation classification for cognitive radio , 2016, 2016 IEEE International Conference on Communication Systems (ICCS).

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Xiao Peng,et al.  Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels , 2014, EURASIP J. Wirel. Commun. Netw..

[10]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[11]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[12]  Georgios B. Giannakis,et al.  Cooperative Spectrum Sensing for Cognitive Radios Using Kriged Kalman Filtering , 2009, IEEE Journal of Selected Topics in Signal Processing.

[13]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[14]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[15]  Jeffrey G. Andrews,et al.  Spectrum Sharing for Device-to-Device Communication in Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

[16]  Simon Haykin,et al.  Spectrum Sensing for Cognitive Radio , 2009, Proceedings of the IEEE.

[17]  Sangarapillai Lambotharan,et al.  Game theoretic analysis for MIMO radars with multiple targets , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[19]  Richard G. Baraniuk,et al.  Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[21]  Sangarapillai Lambotharan,et al.  Variational Bayesian learning technique for spectrum sensing in cognitive radio networks , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[22]  Sangarapillai Lambotharan,et al.  Cooperative spectrum sensing in cognitive radio networks using multi-class support vector machine algorithms , 2015, 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS).

[23]  Sangarapillai Lambotharan,et al.  Kalman Filter Enhanced Parametric Classifiers for Spectrum Sensing Under Flat Fading Channels , 2015, CrownCom.

[24]  Timothy J. O'Shea,et al.  Deep architectures for modulation recognition , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[25]  Olav Tirkkonen,et al.  Spectrum Sensing in the Presence of Multiple Primary Users , 2012, IEEE Transactions on Communications.

[26]  Olusegun Peter Awe,et al.  Machine learning algorithms for cognitive radio wireless networks , 2015 .

[27]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[28]  Takeo Fujii,et al.  Modulation classification for cognitive radios using stacked denoising autoencoders , 2017, Int. J. Satell. Commun. Netw..

[29]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[30]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Qihui Wu,et al.  Spatial-Temporal Opportunity Detection for Spectrum-Heterogeneous Cognitive Radio Networks: Two-Dimensional Sensing , 2013, IEEE Transactions on Wireless Communications.

[32]  Danijela Cabric,et al.  Spatio-temporal spectrum sensing using distributed antenna systems and direct localization methods , 2012, Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation.

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[34]  Sofie Pollin,et al.  Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors , 2017, ArXiv.