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Cooperative MIMO spectrum sensing based on random matrix theory

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Journal of Electronics (China)

Abstract

Random Matrix Theory (RMT) is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large matrices. In this paper, using asymptotic random matrix theory, a new cooperative Multiple-Input Multiple-Output (MIMO) scheme for spectrum sensing is proposed, which shows how asymptotic free property of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for Cognitive Radios (CRs). Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance compared with the energy detection techniques even in the case of a small sample of observations.

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References

  1. A. M. Tulino and S. Verdu. Random Matrix Theory and Wireless Communications. Hanover, now Publishers Inc., 2004, 9–186.

  2. M. L. Mehta. Random Matrices. 3rd ed., London, Academic Press, 2006, 134–338.

    Google Scholar 

  3. Yingchang Liang, Guangming Pan, and Z. D. Bai. Asymptotic performance of MMSE receivers for large systems using random matrix theory. IEEE Transactions on Information Theory, 53(2007)11, 4173–4189.

    Article  MathSciNet  Google Scholar 

  4. Jack W. Silverstein and Antonia M. Tulino. Theory of large dimensional random matrices for engineers. IEEE Ninth International Symposium on Spread Spectrum Techniques and Applications, Manaus, Nov. 2006, 458–464.

  5. Zhidong Bai and Jack W. Silverstein. Spectral Analysis of Large Dimensional Random Matrices. Beijing, Science Press, 2006, 158–362.

    MATH  Google Scholar 

  6. Truhachev Dmitri, Schlegel Christian, and Krzymien Lukasz. Low-complexity capacity achieving two-stage d Demodulation/decoding for random matrix channels. IEEE ITW’ 07, California, Sept. 2007, 584–589.

  7. Leonardo S. Cardoso, Merouane Debbah, and Pascal Bianchi. Cooperative spectrum sensing using random matrix theory. ISWPC, Santorini, May 2008, 334–338.

    Google Scholar 

  8. Yonghong Zeng and Yingchang Liang. Maximum-minimum eigenvalue detection for cognitive radio. The 18th Annual IEEE International Symposium on PIMRC, Athens, 2007, 81–85.

  9. Lei Wang and Baoyu Zheng. Spectrum sensing based on random matrix theory. IEEE Communication Society Nanjing Chapter, The Workshop on Cognitive Wireless Networks Proceedings, Nanjing, Sept. 2008, 130–134.

  10. F. Hiai and D. Petz. The Semicircle Law, Free Random Variables and Entropy. Providence, American Mathematical Society, 2000, 85–94.

  11. N. Rao and A. Edelman. Free probability, sample covariance matrices and signal processing. ICASSP, Toulouse, May 2006, 1001–1004.

  12. Oyvind Ryan and Mérouane Debbah. Free deconvolution for signal processing applications. IEEE International Symposium on Information Theory, Nice, 2007, 1846–1850.

  13. A. Nica and R. Speicher. Lectures on the combinatorics of free probability. London, Cambridge University Press, 2006, 344–349.

    Book  MATH  Google Scholar 

  14. D. Voiculescu. Addition of certain non-commuting random variables. Journal of Functional Analysis, 66(1986)1, 323–335.

    Article  MATH  MathSciNet  Google Scholar 

  15. D. V. Voiculescu. Multiplication of certain non-commuting random variables. Journal of Operator Theory, 18(1987)2, 223–235.

    MATH  MathSciNet  Google Scholar 

  16. Y. C. Liang, S. Sun, and C. K. Ho. Block-iterative generalized decision feedback equalizers (BI-GDFE) for large MIMO systems: Algorithm design and asymptotic performance analysis. IEEE Transactions on Signal Processing, 54(2006)6, 2035–2048.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommuications, Nanjing, 210003, China

    Lei Wang, Baoyu Zheng & Jingwu Cui

  2. Department of Electrical and Computer Engineering Hoboken, Stevens Institute of Technology, New Jersey, 07030, USA

    Chao Chen

  3. Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Lei Wang

Authors
  1. Lei Wang
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  2. Baoyu Zheng
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  3. Jingwu Cui
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  4. Chao Chen
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Corresponding author

Correspondence to Lei Wang.

Additional information

Supported by the National Natural Science Foundation of China (No. 60972039), Natural Science Foundation of Jiangsu Province (No. BK2007729), and Natural Science Funding of Jiangsu Province (No. 06KJA51001).

Communication author: Wang Lei, born in 1977, male, Ph.D..

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Wang, L., Zheng, B., Cui, J. et al. Cooperative MIMO spectrum sensing based on random matrix theory. J. Electron.(China) 27, 190–196 (2010). https://doi.org/10.1007/s11767-010-0306-8

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  • DOI: https://doi.org/10.1007/s11767-010-0306-8

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