Steven Hong (hsiying@stanford.edu)
Project
Proposal (PDF)
Project
Report (In Progress)
The
proliferation of wireless devices and services the past two decades has
necessitated flexible and efficient use of the spectrum, which is becoming a
scarce natural resource. Recently, Cognitive Radio
(CR) has emerged as a promising candidate to resolve the impending spectral
drought [1]. The premise of Cognitive Radio is that it can sense and adapt to the environment, enabling it to maximize the
efficiency of a wireless system by detecting and transmitting on underused
frequency bands while avoiding interference with the primary user. One
of the main bottlenecks to the realization of such a system is the development
of a computationally efficient method to detect and classify what signals are
present in an ultra-wide band frequency. Previous
works have shown that single signals can be classified by exploiting cyclostationarity [2-7], but these techniques have proven
to be either too computationally complex or too functionally limited. Using
this analysis as a foundation, we propose to develop a computationally
efficient cyclic spectral analysis based on the fundamentals of compressed
sensing.
[1]
J. Mitola, “Cognitive Radio an integrated agent
architecture for software defined radio, “ Ph.D dissertation, KTH Royal Institute of
Technology, Stockholm, Sweden, 2000.
[2] C.
Tekin, S. Hong, W. Stark, “ Enhancing Cognitive Radio
Dynamic Spectrum Sensing through Adaptive Learning.” IEEE MilCOM ’09, October 2009.
[3] S.
Hong, E. Like, Z. Wu, C. Tekin “Multi-User Signal Classification via Spectral
Correlation” IEEE CCNC ’10, January
2010.
[4] W.
Gardner, “Exploitation of Spectral Redundancy in Cyclostationary Signals.” IEEE Signal Processing Magazine, pp.
14-36, April 2009
[5] E.
Azzouz, A. Nandi, “Automatic modulation recognition
of communication signals” Kluwer Academic
Publishers, 1996.
[6] W.
Su, J. Kosinski, “Comparison and modification of
automated communication modulation recognition methods.” IEEE MilCOM ’02, October 2002.
[7] W.
Gardner, “An Introduction to Cyclostationary Signals”, Chapter 1,
Cyclostationary in Communications and Signal Processing, IEEE Press,
Piscataway, NJ, 1993.
[8] E.
Da Costa, “Detection and Identification of
Cyclostationary Signals”, Master’s Thesis, Naval Postgraduate School, Monterey,
California, USA, 1996.
[9] M.
Mishali, Y. Eldar,“Blind Multi-Band Signal Reconstruction: Compressed
Sensing for Analog Signals”, IEEE Transactions
on Signal Processing, v 57, n 3, p 993-1009, 2009
[10] Y. Eldar, “Compressed Sensing
of Analog Signals in Shift-Invariant Spaces”, IEEE Transactions on Signal
Processing, v 57, n 8, p 2986-2997, 2009