CS  Vol.6 No.10 , October 2015
Computer Platform Adaptive Interference Cancellation Using Higher-Order Statistics
ABSTRACT
Broadband wireless interference in a computer platform is the result of multiple dynamic electromagnetic emission sources. This interference is non-Gaussian and a receiver design based on the Gaussian assumption will yield suboptimal performance. In fact, it has a double-sided K-distribution and needs to be treated differently in the design process. When dealing with this type of interference in the presence of white Gaussian noise, traditional interference/noise cancellation schemes do not produce satisfactory results. In this paper, we present an interference mitigation method which improves BER performance. We do this by using the cross-cumulant as the criterion of goodness. Specifically, our algorithm is based on higher order statistics (HOS) and is designed to reconstruct and to cancel the interference in a recursive fashion. The algorithm is tested on both BPSK and OFDM communication environments. We compare performance in terms of BER against other cancellation methods.

Cite this paper
Wang, Q. , Magaña, M. and Skinner, H. (2015) Computer Platform Adaptive Interference Cancellation Using Higher-Order Statistics. Circuits and Systems, 6, 201-212. doi: 10.4236/cs.2015.610021.
References
[1]   Parkvall, S. and Astely, D. (2009) The Evolution of LTE towards IMT-Advanced. Journal of Communications, 4, 146-154.
http://dx.doi.org/10.4304/jcm.4.3.146-154

[2]   (2011) IEEE Standard for Information Technology—Local and Metropolitan Area Networks—Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 8: IEEE 802.11 Wireless Network Management. IEEE Std. 802.11v-2011, New York, 9 February 2011, 1-433.

[3]   Federal Communications Commission (2012) Radiated Emission Limits. Code of Federal Regulations, 109, 800.

[4]   Alban, E.X., Magana, M.E., Skinner, H.G. and Slattery, K.P. (2012) Statistical Modeling of the Interference Noise Generated by Computing Platforms. IEEE Transactions on Electromagnetic Compatibility, 54, 574-584.

[5]   Alban, E.X. (2011) Immune Radio Architecture for Platform Interference. PhD Thesis, Oregon State University, Corvallis.

[6]   Kay, S. (2010) Representation and Generation of Non-Gaussian Wide-Sense Stationary Random Processes with Arbitrary PSDs and a Class of PDFs. IEEE Transactions on Signal Processing, 58, 3448-3458.

[7]   Shin, D.C. and Nikias, C.L. (1994) Adaptive Interference Canceler for Narrowband and Wideband Interferences Using Higher Order Statistics. IEEE Transactions on Signal Processing, 42, 2715-2728.
http://dx.doi.org/10.1109/78.324737

[8]   Iskander, D.R. and Zoubir, A.M. (1999) Estimation of the Parameters of the K-Distribution Using Higher Order and Fractional Moments. Transactions on Aerospace and Electronic Systems, 35, 1453-1457.
http://dx.doi.org/10.1109/7.805463

[9]   Nikias, C.L. and Petropulu, A.P. (1993) Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework. PTR Prentice Hall, Inglewood Cliffs.

[10]   Nikias, C.L. and Raghuveer, M.R. (1987) Bispectrum Estimation: A Digital Signal Processing Framework. Proceedings of the IEEE, 75, 869-891.
http://dx.doi.org/10.1109/PROC.1987.13824

 
 
Top