Signal Detection in an Impulsive Noise Environment Using Locally Optimum Detection

Locally optimum detection is a technique for reliable signal estimation in the presence of strong non-Gaussian interference. It is particularly suited to direct-spread spectrum systems due to the diversity gain achieved because of spreading. However, even more gains can possibly be derived by performing this operation in an iterative fashion, thereby allowing signal detection at even lower signal-to-interference ratios. In this paper the locally optimum detector used in an iterative scheme to suppress strong non-Gaussian interference is studied. The interference is modeled as Middleton class-A and the data stream is convolutionally encoded. Simulation results demonstrating performance improvement over a simple linear combiner are presented.

[1]  L. B. Milstein,et al.  Interference rejection techniques in spread spectrum communications , 1988, Proc. IEEE.

[2]  Subbarayan Pasupathy,et al.  Reduced complexity symbol detectors with parallel structures , 1990, [Proceedings] GLOBECOM '90: IEEE Global Telecommunications Conference and Exhibition.

[3]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[4]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[5]  H. Vincent Poor,et al.  A promising multiplexing technology for cellular telecommunications: Narrowband Interference Suppression in Spread Spectrum CDMA , 1994, IEEE Personal Communications.

[6]  Jeffrey H. Reed,et al.  Interference rejection in digital wireless communications , 1997, IEEE Signal Process. Mag..

[7]  J. Doherty,et al.  Robust Interference Suppression in Spread Spectrum Systems using Locally Optimum Detection , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[8]  S. Kassam.  Signal Detection in Non-Gaussian Noise , 1987 .

[9]  J. L. Melsa,et al.  Decision and Estimation Theory , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  D. Middleton,et al.  Optimum Reception in an Impulsive Interference Environment - Part II: Incoherent Reception , 1977, IEEE Transactions on Communications.

[11]  L. Berry.  Understanding Middleton's Canonical Formula for Class a Noise , 1981, IEEE Transactions on Electromagnetic Compatibility.

[12]  Jenq-Neng Hwang,et al.  Nonparametric multivariate density estimation: a comparative study , 1994, IEEE Trans. Signal Process..

[13]  P. A. Delaney,et al.  Signal detection in multivariate class-A interference , 1995, IEEE Trans. Commun..

[14]  Nenad Veselinovic,et al.  Iterative receivers for interference cancellation and suppression in wireless communications , 2004 .

[15]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[16]  Wolfgang Koch,et al.  Optimum and sub-optimum detection of coded data disturbed by time-varying intersymbol interference (applicable to digital mobile radio receivers) , 1990, [Proceedings] GLOBECOM '90: IEEE Global Telecommunications Conference and Exhibition.

[17]  Kenneth S. Vastola,et al.  Threshold Detection in Narrow-Band Non-Gaussian Noise , 1984, IEEE Trans. Commun..

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

[19]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[20]  A. Spaulding,et al.  Optimum Reception in an Impulsive Interference Environment - Part I: Coherent Detection , 1977, IEEE Transactions on Communications.