Soft-Decision Metrics for Coded Orthogonal Signaling in Symmetric Alpha-Stable Noise

This paper derives new soft decision metrics for coded orthogonal signaling in symmetric /spl alpha/-stable noise, which has been used to model impulsive noise. In addition to the optimum metrics for Gaussian (/spl alpha/=2) noise and Cauchy (/spl alpha/=1) noise, a class of generalized likelihood ratio (GLR) metrics with lower side information requirements is derived. Through numerical results for a turbo code example, the Cauchy decoder is found to be robust for a wide range of /spl alpha/, and GLR metrics are found which provide performance gains relative to the Gaussian metric, but with lower complexity and less a priori information.