Evidence-Theory-Based Cooperative Spectrum Sensing With Efficient Quantization Method in Cognitive Radio

Sensing spectra in a reliable and efficient manner is fundamental to cognitive radio (CR). Ensuring cooperation among spectrum sensing devices is an appropriate method when a CR system is under deep shadowing and in a fading environment. In this paper, an enhanced scheme for cooperative spectrum sensing (CSS) based on efficient quantization and the Dempster-Shafer (D-S) theory of evidence is proposed. The proposed scheme includes an effective quantizer for the sensing data by utilizing special properties of the hypothesis distribution under different signal-to-noise ratios (SNRs) of the primary signal. As a result, the required bandwidth for the reporting channel is reduced while the advantage for combinations of the D-S theory is maintained. Simulation results revealed that significant improvements in the CSS gain, as well as a reduction in the system overhead, were achieved.

[1]  Przemyslaw Pawelczak,et al.  Multinode Spectrum Sensing Based on Energy Detection for Dynamic Spectrum Access , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[2]  Yan Jiang,et al.  Data Fusion Using Improved Dempster-Shafer Evidence Theory for Vehicle Detection , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[3]  Jun Wang,et al.  Cooperative Spectrum Sensing with Multi-Bits Local Sensing Decisions in Cognitive Radio Context , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[4]  Ranjan K. Mallik,et al.  Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks , 2008, 2008 IEEE International Conference on Communications.

[5]  Lei Chen,et al.  An Adaptive Cooperative Spectrum Sensing Scheme Based on the Optimal Data Fusion Rule , 2007, 2007 4th International Symposium on Wireless Communication Systems.

[6]  Bin Liu,et al.  Channel-optimized quantizers for decentralized detection in sensor networks , 2006, IEEE Transactions on Information Theory.

[7]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[8]  Rick S. Blum,et al.  Distributed Random Signal Detection with Multibit Sensor Decisions , 1998, IEEE Trans. Inf. Theory.

[9]  Stephen J. Shellhammer,et al.  Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs , 2006, TAPAS '06.

[10]  Ping Wang.  The Reliable Combination Rule of Evidence in Dempster-Shafer Theory , 2008, 2008 Congress on Image and Signal Processing.

[11]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Patrick Duvaut,et al.  Optimum quantization for detection , 1988, IEEE Trans. Commun..

[13]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[14]  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.

[15]  L.J. Greenstein,et al.  An empirically-based path loss model for wireless channels in suburban environments , 1998, IEEE GLOBECOM 1998 (Cat. NO. 98CH36250).

[16]  Mourad Barkat,et al.  Signal detection and estimation , 1991 .

[17]  Jingyu Hua,et al.  An adaptive signal-to-noise ratio estimator in mobile communication channels , 2010, Digit. Signal Process..

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

[19]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

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

[21]  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..

[22]  H. Saarnisaari,et al.  A Blind Signal Localization and SNR Estimation Method , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[23]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[24]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[25]  Tao Jiang,et al.  Maximum channel throughput via cooperative spectrum sensing in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[26]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Wang Jun,et al.  A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[28]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[29]  Saleem A. Kassam,et al.  Optimum Quantization for Signal Detection , 1977, IEEE Trans. Commun..

[30]  Ge Lindong,et al.  A Novel Blind SNR Estimator Based on the Modified PASTd Algorithm for IF Signals , 2006, 2006 International Conference on Systems and Networks Communications (ICSNC'06).