Performance analysis and optimization schemes for cooperative spectrum sensing and information fusion for cognitive radio : A survey

Accurate spectrum sensing plays a decisive role in determining the performance of any cognitive radio network (CRN). Spectrum sensing enables the unlicensed secondary users (SUs) to operate in the licensed band until the primary user (PU) is detected. By allowing SUs in the same band to cooperate, we can reduce the detection time and increase overall agility and this forms the basis of cooperative spectrum sensing (CSS) wherein a collaborative decision on the state of PU is made by combining the local sensing information gathered by the individual SU nodes. The unification of individual sensing results to form a global decision is done through information fusion techniques. This work aims at presenting state-of-the-art proposals and optimization approaches for cooperative spectrum sensing and information fusion in cognitive radio environment.

[1]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[2]  Md. Shamim Hossain,et al.  Hard Decision Based Cooperative Spectrum Sensing over Different Fading Channel in Cognitive Radio , 2013 .

[3]  K. J. Ray Liu,et al.  Evolutionary cooperative spectrum sensing game: how to collaborate? , 2010, IEEE Transactions on Communications.

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

[5]  Ghaith Hattab,et al.  Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining , 2018, IEEE Systems Journal.

[6]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2007, IEEE Transactions on Wireless Communications.

[7]  Zhimin Zeng,et al.  A Study of Data Fusion and Decision Algorithms Based on Cooperative Spectrum Sensing , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[8]  Sundeep Prabhakar Chepuri,et al.  Optimal hard fusion strategies for cognitive radio networks , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[9]  Janne J. Lehtomäki,et al.  On the Selection of the Best Detection Performance Sensors for Cognitive Radio Networks , 2010, IEEE Signal Processing Letters.

[10]  Shuguang Cui,et al.  Collaborative wideband sensing for cognitive radios , 2008, IEEE Signal Processing Magazine.

[11]  Brian M. Sadler,et al.  COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space , 2007, IEEE Communications Magazine.

[12]  Yonghong Zeng,et al.  Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View , 2009, IEEE Transactions on Vehicular Technology.

[13]  Soo Young Shin,et al.  Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks , .

[14]  Sanjay Dhar Roy,et al.  Performance of cooperative spectrum sensing with soft data fusion schemes in fading channels , 2013, 2013 Annual IEEE India Conference (INDICON).

[15]  Ying-Chang Liang,et al.  Optimization for Cooperative Sensing in Cognitive Radio Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[16]  Yang Zheng,et al.  Improved hard-decision fusion algorithm against SSDF in cognitive radio networks , 2015 .

[17]  Zhengding Qiu,et al.  Asynchronous cooperative spectrum sensing in cognitive radio , 2008, 2008 9th International Conference on Signal Processing.

[18]  Yan Zhang,et al.  A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks , 2010, IEEE Transactions on Vehicular Technology.

[19]  Zhu Han,et al.  Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks , 2009, IEEE INFOCOM 2009.

[20]  B. Scheers,et al.  Data fusion schemes for cooperative spectrum sensing in cognitive radio networks , 2012, 2012 Military Communications and Information Systems Conference (MCC).