A blind receiver with multiple antennas in impulsive noise with Gaussian mixtures through MCMC approaches

Conventional Multi-Input Multi-Output (MIMO) receivers in the presence of Gaussian noise meet the performance degradation in ELF/VLF communication system, in which noise is highly impulsive. A blind receiver with an antenna array is developed to estimate symbols and channel model parameters simultaneously in impulsive noise environments. The noise is modeled as a mixture of multivariate Gaussian distributions, which is capable of representing abroad class of non-Gaussian distributions. The parameters of channel model and recipient symbols are estimated blindly through the Markov Chain Monte Carlo (MCMC) algorithm. Simulations reveal that the blind receiver proposed can achieve near optimum performance.

[1]  Arnaud Doucet,et al.  On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods , 2009, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[2]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[3]  Cliburn Chan,et al.  Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures , 2010, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[4]  David Middleton,et al.  Statistical-Physical Models of Electromagnetic Interference , 1977, IEEE Transactions on Electromagnetic Compatibility.

[5]  Robert W. Heath,et al.  MIMO Receiver Design in the Presence of Radio Frequency Interference , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[6]  T. W. Anderson An Introduction to Multivariate Statistical Analysis, 2nd Edition. , 1985 .

[7]  Rick S. Blum,et al.  A statistical and physical mechanisms-based interference and noise model for array observations , 2000, IEEE Trans. Signal Process..

[8]  Arthur A. Giordano,et al.  Statistical Characterization of Impulsive Noise , 2003 .

[9]  Rick S. Blum,et al.  An adaptive receiver with an antenna array for channels with correlated non-Gaussian interference and noise using the SAGE algorithm , 2000, IEEE Trans. Signal Process..

[10]  David Middleton,et al.  Non-Gaussian Noise Models in Signal Processing for Telecommunications: New Methods and Results for Class A and Class B Noise Models , 1999, IEEE Trans. Inf. Theory.

[11]  Brian M. Sadler,et al.  Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures , 2000, IEEE Trans. Signal Process..