Boost the efficiency of spectrum sensing using synchronized random demodulation

In many applications, performing spectrum sensing after sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. For this type of signal, we focus on improve Random Demodulation, a novel technology inspired by Compressed Sensing theory to achieve sub-Nyquist sampling and boosting the efficiency of spectrum sensing. So, Synchronized Random Demodulation technology is proposed and a corresponding system is designed in this paper. It is noticed that two key elements play crucial role in this sensing system: synchronization among signals and accurate system impulse response. Then, a specialized synchronization mechanism and a cross-correlation method for measuring SRD system impulse response is developed to improve the system performance. Experiments demonstrate that the sampling rates for successful spectrum sensing and signal recovery can be as low as 4% of Nyquist rate.

[1]  Gang Wang,et al.  A random demodulation hardware system with automatic synchronization function , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[2]  Yonina C. Eldar,et al.  Wideband Spectrum Sensing at Sub-Nyquist Rates [Applications Corner] , 2010, IEEE Signal Processing Magazine.

[3]  Ning Xiang Using M-sequences for determining the impulse responses of LTI-systems , 1992, Signal Process..

[4]  Yonina C. Eldar,et al.  Wideband Spectrum Sensing at Sub-Nyquist Rates , 2010, ArXiv.

[5]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[6]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[7]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[8]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[9]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[10]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.