A weighted fusion scheme for cooperative spectrum sensing based on past decisions

Cooperative spectrum sensing is employed in cognitive radio networks to reliably detect the primary users' transmissions by fusing the sensed data of individual secondary users. In this paper, we propose a new weighted fusion scheme, in which the reliability of the secondary users' local decisions are considered when making a final decision at the fusion center. We use past information about the local and global decisions to estimate the reliability of the sensing decision obtained from each secondary user. This difference in reliability is reflected in the weighting of each secondary user's decision when combined at the fusion center. Simulations are provided to compare the performance of the proposed scheme to the OR, AND, Equal Weight and Signal-to-Noise Ratio (SNR) based Weight fusion schemes. Results show that our proposed scheme provides performance improvement for cooperative spectrum sensing when compared to the other fusion schemes.

[1]  Yonghong Zeng,et al.  Cooperative Spectrum Sensing in Cognitive Radio Networks with Weighted Decision Fusion Scheme , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[2]  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..

[3]  Jun Wang,et al.  A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context , 2006, PIMRC.

[4]  Yonghong Zeng,et al.  Cooperative Spectrum Sensing in Cognitive Radio Networks with Weighted Decision Fusion Schemes , 2010, IEEE Transactions on Wireless Communications.

[5]  N. Ansari,et al.  Adaptive fusion by reinforcement learning for distributed detection systems , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Insoo Koo,et al.  An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context , 2009, IEEE Communications Letters.

[7]  Huseyin Arslan,et al.  Cognitive radio, software defined radio, and adaptiv wireless systems , 2007 .

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

[9]  Shuguang Cui,et al.  Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE Journal of Selected Topics in Signal Processing.

[10]  Yong-Hwan Lee,et al.  Linear Hard Decision Combining for Cooperative Spectrum Sensing in Cognitive Radio Systems , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[11]  Insoo Koo,et al.  A Secure Distributed Spectrum Sensing Scheme in Cognitive Radio , 2009, ICIC.

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

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