Suboptimal Detectors for Alpha-Stable Noise: Simplifying Design and Improving Performance

Abstract

The design of detectors for binary signals in symmetric alpha-stable noise is considered. Since the optimal detector is impractically complex, many suboptimal detectors have been proposed such as the Gaussian, soft limiter, myriad and Cauchy detectors. However, no adequate explanation for the difference in performance between these detectors has been proposed. In this paper, we propose a novel framework, based on the optimal decision regions, that is used to justify the performance of many suboptimal detectors and compare them to the optimal one. Moreover, the analysis of the framework provides a novel method to significantly improve the performance of the soft limiter detector by employing an adaptive threshold that is a function of the signal level. As the number of samples per symbol increases, the performance of the proposed adaptive detector approaches the optimal performance at almost no additional complexity over the conventional Gaussian detector.