ChiMaS: A spectrum sensing-based channels classification system for cognitive radio networks

Cognitive radio devices are able to sense the spectrum of frequencies and share access to vacant channels. These devices usually have a candidate channels list that must be sensed to find a vacant channel. In this paper, we propose a novel system called ChiMaS, which is able to manage the candidate channels list implementing three tasks: Analysis, Creation, and Sort. Analysis applies reinforcement learning algorithms to evaluate the channels quality based on their historical occupancy and their conditions; Creation is responsible for creating the Candidate Channels List; and Sort ranks the channels to obtain an Ordered Channels List in terms of quality. Results show that ChiMaS manages the candidate channels list following the IEEE 802.22 definition, while it finds the best channel in terms of availability and quality faster than Q-Noise+ algorithm, which was implemented for comparison purpose.

[1]  José Luis Ayala,et al.  A Link Quality Estimator for Power-Efficient Communication Over On-Body Channels , 2014, 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing.

[2]  H. Vincent Poor,et al.  Optimal selection of channel sensing order in cognitive radio , 2009, IEEE Transactions on Wireless Communications.

[3]  Kamran Arshad,et al.  Order-Statistic Based Spectrum Sensing for Cognitive Radio , 2012, IEEE Communications Letters.

[4]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[5]  Dharma P. Agrawal,et al.  A framework for statistical wireless spectrum occupancy modeling , 2010, IEEE Transactions on Wireless Communications.

[6]  Lisandro Zambenedetti Granville,et al.  Adaptive threshold architecture for spectrum sensing in public safety radio channels , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[7]  Lisandro Zambenedetti Granville,et al.  Improving reinforcement learning algorithms for dynamic spectrum allocation in cognitive sensor networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Sinem Coleri Ergen,et al.  Spatio-temporal characteristics of link quality in wireless sensor networks , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

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

[10]  Qinyu Zhang,et al.  Model free dynamic sensing order selection for imperfect sensing multichannel cognitive radio networks: A Q-learning approach , 2014, 2014 IEEE International Conference on Communication Systems.

[11]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[12]  José Ferreira de Rezende,et al.  Channel sensing order for cognitive radio networks using reinforcement learning , 2011, 2011 IEEE 36th Conference on Local Computer Networks.