MIMO-based Multitaper detection over Nakagami channels for dynamic spectrum access devices

The multitaper estimator is considered as the most powerful nonparametric method for reconstructing the power spectrum of a signal. The multitaper detector has been strongly recommended to be used for spectrum sensing in cognitive radio systems. In this paper we provide a new and accurate model for the Multitaper detector assuming that both the transmitting and detecting nodes are employing single-user multiple-input-multiple-output (MIMO) structures. We present closed form mathematical expressions for the performance of the decision variable within the hypotheses testing context. We model the decision variable using the Phase-Type distribution, where we derive the exact distribution parameters for both the null and the alternate hypotheses. Furthermore, we accurately bound the average probability of detection over Nakagami fading channels. Finally, the average probability of detection is maximized to yield a predetermined probability of false alarm. The results show that the obtained analytical models are accurate. As a generic trend, it is found that adjusting the length of observed sequences has no effect on the detector performance. On the other hand, it is found that increasing the number of receiving branches provides a significant enhancement for the MIMO-Multitaper method.

[1]  Jeffrey D. Scargle,et al.  Nonlinear and Nonstationary Signal Processing , 2002, Technometrics.

[2]  Ieee Staff,et al.  2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) , 2015 .

[3]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[4]  Ronald F. Boisvert,et al.  NIST Handbook of Mathematical Functions , 2010 .

[5]  Weifang Wang,et al.  Spectrum sensing in cognitive radio , 2016 .

[6]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[7]  W. Marsden I and J , 2012 .

[8]  Emad Alsusa,et al.  On the Performance of Energy Detection Using Bartlett's Estimate for Spectrum Sensing in Cognitive Radio Systems , 2012, IEEE Transactions on Signal Processing.

[9]  Donald B. Percival,et al.  Spectral Analysis for Physical Applications , 1993 .

[10]  Emad Alsusa,et al.  Performance Analysis of the Periodogram-Based Energy Detector in Fading Channels , 2011, IEEE Transactions on Signal Processing.

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

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

[13]  Emad Alsusa,et al.  New and accurate results on the performance of the Multitaper-based detector , 2012, 2012 IEEE International Conference on Communications (ICC).

[14]  V. Tarokh,et al.  Cognitive radio networks , 2008, IEEE Signal Processing Magazine.