Nonlinear optimization for adaptive antenna array receivers with a small data-record size

Design of a nonlinear adaptive antenna array receiver is a challenging task in wireless communications due to the limited number of antenna elements and the presence of correlated signals, which directly affect the performance of an antenna array. More importantly, a conventional nonlinear array receiver is often associated with a high computational complexity that undermines its applicability in practice. In this paper, we present a new approach to adaptive beamforming receiver that provides superior performance in antenna array overloading and in the presence of correlated signals with a low complexity. In particular, the proposed receiver requires a small data-record size to estimate the beamformer weights, which is beneficial in applications with fast fading channels. Simulation examples illustrate the performance improvement of the proposed array receiver when it is compared to the conventional beamformers. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  James A. Bucklew,et al.  Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..

[2]  O. Bousquet,et al.  Kernel methods and their potential use in signal processing , 2004, IEEE Signal Processing Magazine.

[3]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[4]  B. Carlson Covariance matrix estimation errors and diagonal loading in adaptive arrays , 1988 .

[5]  Thomas Kailath,et al.  Adaptive beamforming for coherent signals and interference , 1985, IEEE Trans. Acoust. Speech Signal Process..

[6]  Thomas Kailath,et al.  Performance analysis of the optimum beamformer in the presence of correlated sources and its behavior under spatial smoothing , 1987, IEEE Trans. Acoust. Speech Signal Process..

[7]  Jerry M. Mendel,et al.  Applications of cumulants to array processing - I. Aperture extension and array calibration , 1995, IEEE Trans. Signal Process..

[8]  Jerry M. Mendel,et al.  Applications of cumulants to array processing. III. Blind beamforming for coherent signals , 1997, IEEE Trans. Signal Process..

[9]  Christopher J. C. Burges,et al.  Simplified Support Vector Decision Rules , 1996, ICML.

[10]  Lajos Hanzo,et al.  Support vector machine multiuser receiver for DS-CDMA signals in multipath channels , 2001, IEEE Trans. Neural Networks.

[11]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[12]  Gert Cauwenberghs,et al.  Sequence estimation and channel equalization using forward decoding kernel machines , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Stella N. Batalama,et al.  Recursive short-data-record estimation of AV and MMSE/MVDR linear filters for DS-CDMA antenna array systems , 2004, IEEE Transactions on Communications.

[14]  Koby Crammer,et al.  On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[15]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.