Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
권호 표지
DOI QR코드

DOI QR Code

Blind Signal Processing for Wireless Sensor Networks

  • Kim, Namyong (Division of Electronic Information and Communication Engineering, Kangwon National University) ;
  • Byun, Hyung-Gi (Division of Electronic Information and Communication Engineering, Kangwon National University)
  • Received : 2014.04.10
  • Accepted : 2014.05.22
  • Published : 2014.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

In indoor sensor networks equalization algorithms based on the minimization of Euclidean distance (MED) for the distributions of constant modulus error (CME) have yielded superior performance in compensating for signal distortions induced from optical fiber links, wireless-links and for impulsive noise problems. One main drawback of MED-CME algorithms is a heavy computational burden hindering its implementation. In this paper, a recursive gradient estimation for weight updates of the MED-CME algorithm is proposed for reducing the operations $O(N^2)$ of the conventional MED-CME to O(N) at each iteration time for N data-block size. From the simulation results of the proposed recursive method producing exactly the same results as the conventional method, the proposed estimation method can be considered to be a reliable candidate for implementation of efficient receivers in indoor sensor networks.

Keywords

References

  1. I. Akyildiz, W. Su, Y. Sankarasubramianiam, and E. Cayirci, "A survey on sensor networks", IEEE Commun. Mag., Vol. 40, pp.102-114, 2002.
  2. K. Blackard, T. Rappaport, and C. Bostian, "Measurements and models of radio frequency impulsive noise for indoor wireless communications", IEEE J. Sel. Areas in Commun., Vol. 11, pp. 991-1001, 1993. https://doi.org/10.1109/49.233212
  3. X. Li, "Blind channel estimation and equalization in wireless sensor networks based on correlations among sensors", IEEE Trans. Signal Processing, Vol. 53, pp. 1511-1519, 2005. https://doi.org/10.1109/TSP.2005.843744
  4. J. Tugnait, L. Tong, and Z. Ding, "Single user channel estimation and equalization", IEEE Signal Process. Mag., Vol. 17, pp. 17-28, 2000.
  5. L. Garth, "A dynamic convergence analysis of blind equalization algorithms", IEEE Trans. on Comm., Vol. 49, pp. 624-634, 2001. https://doi.org/10.1109/26.917769
  6. I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: Definition, properties, and application to blind equalization", IEEE Trans. Signal Processing, Vol. 54, pp. 2187-2197, 2006. https://doi.org/10.1109/TSP.2006.872524
  7. N. Kim, "A new constant modulus algorithm based on minimum Euclidian distance criterion for blind channel equalization", Journal of Korean Society for Internet Information, Vol. 10, pp. 19-26, 2009.
  8. E. Parzen, "On the estimation of a probability density function and the mode", Ann. Math. Stat., Vol. 33, p. 1065, 1962. https://doi.org/10.1214/aoms/1177704472
  9. N. Kim, H. Byun, and J. Lim, "Blind signal processing algorithms under dc biased Gaussian noise", Proc. of SPIE 2013 Nano-Bio Sensing, Imaging & Spectroscopy, p. OC3-5, Jejoo, Korea, 2013.
  10. M. Girolami and C. He, "Probability density estimation from optimally condensed data samples", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 1253-1264, 2003. https://doi.org/10.1109/TPAMI.2003.1233899