Spectrum Sensing using Sparse Bayesian Learning

The availability of the radio spectrum is limited. To compromise the increasing demand for high data rate devices, this fixed spectrum need to be used efficiently. The existing challenge is that the larger portion of the licensed spectrum is underutilized. Cognitive Radio is a technology introduced to help to detect the radio spectrum is occupied or not. Spectrum sensing helps to detect the spectrum holes and provides high spectral resolution capability. This is done using sparse techniques such as Compressive sensing and Sparse Bayesian Learning techniques. Compressive Sensing algorithms such as Basis Pursuit and Orthogonal Matching Pursuit are analyzed. Based on the concept of Sparse Bayesian learning, an expectation maximization algorithm is introduced for spectrum sensing and recovery of the original transmitted signal in cognitive radio systems. Performance comparison is done between proposed algorithms and is validated using Register Transfer Level- Software Defined Radio.

[1]  S. Kirthiga,et al.  An adaptive threshold method for energy based spectrum sensing in Cognitive Radio Networks , 2015, 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[2]  Qun Zhang,et al.  A cognitive signals reconstruction algorithm based on compressed sensing , 2015, 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR).

[3]  Peng-Hua Wang,et al.  Cooperative Spectrum Sensing and Locationing: A Sparse Bayesian Learning Approach , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[4]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[5]  Zhiqiang Wu,et al.  Joint spectrum sensing and primary user localization for cognitive radio via compressed sensing , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[6]  Pallavi Dhole,et al.  Sparse Signal Reconstruction using BasisPursuit Algorithm , 2015 .

[7]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[8]  Weifang Wang,et al.  Spectrum sensing in cognitive radio , 2016 .

[9]  Lars Kai Hansen,et al.  Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines , 2004 .

[10]  Prashant Dwivedy,et al.  Software defined radio based receivers using RTL — SDR: A review , 2017, 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE).

[11]  K. P. Soman,et al.  Spectrum Sensing using Compressed Sensing Techniques for Sparse Multiband Signals , 2012 .

[12]  K. P. Soman,et al.  Low cost digital transceiver design for Software Defined Radio using RTL-SDR , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[13]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[14]  David H. Crawford,et al.  A low-cost desktop software defined radio design environment using MATLAB, simulink, and the RTL-SDR , 2015, IEEE Communications Magazine.