Cooperative Sensing via Sequential Detection

Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to reduce the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the log-likelihood ratio for its every measurement, and the base station sequentially accumulates these log-likelihood statistics and determines whether to stop making measurement. The paper studies how to implement the scheme in a robust manner when the assumed signal models have unknown parameters, such as signal strength and noise variance. These ideas are illustrated through two examples in spectrum sensing. One assumes both the signal and noise are Gaussian distributed, while the other assumes the target signal is deterministic.

[1]  Venugopal V. Veeravalli,et al.  Cooperative Sensing for Primary Detection in Cognitive Radio , 2008, IEEE Journal of Selected Topics in Signal Processing.

[2]  Pham Xuan Quang,et al.  Robust Sequential Testing , 1985 .

[3]  Kazunori Akabane,et al.  A Novel Cooperative Sensing Technique for Cognitive Radio , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[4]  J. Andel.  Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.

[5]  H. Vincent Poor,et al.  Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios , 2009, IEEE Transactions on Signal Processing.

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

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

[8]  R.W. Brodersen,et al.  Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[9]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

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

[11]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[12]  Ali H. Sayed,et al.  Cooperative spectrum sensing via coherence detection , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[13]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

[14]  Georgios B. Giannakis,et al.  A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios , 2006, 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[15]  A. V. D. Vaart,et al.  Asymptotic Statistics: Frontmatter , 1998 .

[16]  A. Sayed,et al.  Cooperative spectrum sensing via sequential detection for cognitive radio networks , 2009, 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications.

[17]  Jonathan S. Adelstein.  Statement of commissioner Jonathan S. Adelstein, Re: Unlicensed operation in the TV broadcast bands; second report and order and memorandum opinion and order, ET Docket no. 04-186 , 2010 .

[18]  Wha Sook Jeon,et al.  Sequential detection of cyclostationary signal for cognitive radio systems , 2009, IEEE Transactions on Wireless Communications.

[19]  Sawasd Tantaratana,et al.  Relative efficiency of the sequential probability ratio test in signal detection , 1978, IEEE Trans. Inf. Theory.

[20]  A. Wald.  On Cumulative Sums of Random Variables , 1944 .

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

[22]  Anant Sahai,et al.  SNR Walls for Signal Detection , 2008, IEEE Journal of Selected Topics in Signal Processing.