Decision Fusion Rules in Wireless Sensor Networks Using Fading Channel Statistics

The problem of fusing decisions trans- mitted over fading and noisy channels in a wireless sensor network is revisited. In a previous work, start- ing with the assumption of perfect channel knowledge, an optimal likelihood ratio (LR) based fusion rule was developed along with three suboptimum statis- tics. In this paper, we present a new LR based fusion rule which requires only the knowledge of channel fading statistics instead of the actual channel coeffi- cients. We show that the equal gain combiner (EGC) and the Chair-Varshney fusion rules are approxima- tions of this new rule at low and high channel SNR values, respectively. This new fusion rule has bet- ter performance than Chair-Varshney and EGC fu- sion rules, outperforms the maximum ratio combiner (MRC) at most practical channel SNR values, and is only slightly worse than the optimal LR based fusion rule.