Abstract
Decision fusion in wireless sensor networks under non-Gaussian noise channels is studied. Based on the parallel fusion model, the likelihood ratio (LR) based fusion rule is represented and considered as an optimal fusion rule. From this rule, we obtain suboptimum rules by utilizing high and low signal-to-noise ratio (SNR) approximations corresponding to tail and central parts of noise distributions, respectively. For the high SNR case two wide classes of distributions corresponding to two types of tails, exponentially-tailed and Pareto type distributions, and for low SNR case smooth densities in their central part are considered and studied to derive alternative rules to the LR rule with symmetric and unimodal assumptions. Performance evaluation for several fusion rules is performed through simulation
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