Sparse Power Angle Spectrum Estimation

A novel method for estimating the power angle spectrum (PAS) is presented that decomposes the true PAS into a small set of basis functions. The basis coefficients for this sparse representation are found by enforcing equality to the covariance or Bartlett PAS subject to a minimum lscr1-norm constraint. The method, referred to as sparse PAS estimation (SPASE), can be implemented conveniently using existing linear-programming (LP) solvers. Further, because only a few clusters are required in the representation, the method enables reduced-complexity stochastic models for the channel and possibly allows reduced overhead in channel feedback schemes. Application of the method to simulated channels and multiple-input multiple-output (MIMO) propagation data demonstrates the utility of the method.

[1]  Reinaldo A. Valenzuela,et al.  Antenna pattern deconvolution for precise incident power density pattern measurement , 2000, VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026).

[2]  R.S. Thoma,et al.  Joint maximum likelihood estimation of specular paths and distributed diffuse scattering , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[3]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[4]  Klaus I. Pedersen,et al.  Power azimuth spectrum in outdoor environments , 1997 .

[5]  Martin Nilsson,et al.  A cluster approach to spatio-temporal channel estimation , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[6]  Mark A. Richards.  Iterative noncoherent angular superresolution , 1988 .

[7]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[8]  Wim Kotterman,et al.  On the Influence of Incomplete Data Models on Estimated Angular Distributions in Channel Characterisation , 2007 .

[9]  Stephen J. Wright,et al.  Pcx User Guide Version 1.1 1 , 1997 .

[10]  M. A. Richards.  Iterative noncoherent angular superresolution (radar) , 1988, Proceedings of the 1988 IEEE National Radar Conference.

[11]  Michael A. Jensen,et al.  Modeling the indoor MIMO wireless channel , 2002 .

[12]  Michael A. Jensen,et al.  Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel , 2000, IEEE Journal on Selected Areas in Communications.

[13]  Klaus I. Pedersen,et al.  Channel parameter estimation in mobile radio environments using the SAGE algorithm , 1999, IEEE J. Sel. Areas Commun..

[14]  Reiner S. Thomä,et al.  Capacity of MIMO systems based on measured wireless channels , 2002, IEEE J. Sel. Areas Commun..

[15]  Jon W. Wallace,et al.  Accurate MIMO Channel Modeling: Correlation Tensor vs. Directional Approaches , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[16]  R. Leahy,et al.  On the design of maximally sparse beamforming arrays , 1991 .

[17]  Michael A. Jensen,et al.  A diffuse multipath spectrum estimation technique for directional channel modeling , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[18]  K. Pedersen,et al.  Angle-of-arrival statistics for low resolution antennas , 2002 .

[19]  D. Zhou,et al.  A Novel Method for Estimating the Power Azimuth Spectrum of the Wireless Channel , 2006, IEEE Antennas and Wireless Propagation Letters.

[20]  Stephen J. Wright,et al.  PCx user guide , 1997 .

[21]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[22]  J. Fuchs.  SOME FURTHER RESULTS ON THE RECOVERY ALGORITHMS , 2005 .

[23]  H. Ozcelik,et al.  Variation of measured indoor MIMO capacity with receive direction and position at 5.2 GHz , 2002 .

[24]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[25]  Jean-Jacques Fuchs,et al.  On sparse representations in arbitrary redundant bases , 2004, IEEE Transactions on Information Theory.

[26]  J. Wallace,et al.  Power and complex envelope correlation for modeling measured indoor MIMO channels: a beamforming evaluation , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[27]  Thomas Kailath,et al.  ESPRIT-estimation of signal parameters via rotational invariance techniques , 1989, IEEE Trans. Acoust. Speech Signal Process..

[28]  B. D. Steinberg,et al.  Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique , 1988 .

[29]  C. Guillemot,et al.  Fast implementation of a l∞- l/1 penalized sparse representations algorithm: applications in image denoising and coding , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[30]  M.A. Jensen,et al.  Time-Varying MIMO Channels: Measurement, Analysis, and Modeling , 2006, IEEE Transactions on Antennas and Propagation.