Channel-optimized quantizers for decentralized detection in sensor networks

Motivated by the delay and resource constraints omnipresent in most wireless sensor network applications, we design channel-optimized scalar quantizers for a canonical decentralized detection system. Aimed at minimizing the error probability of the fusion center output, we first establish the optimality of monotone likelihood ratio partition of the observation space for the local quantizer design. We then devise an iterative algorithm to construct distributed quantizers that are person-by-person optimal. The channel-optimized approach is shown to offer better performance compared with various alternatives. It also exhibits inherent adaptivity in resource (bit) allocation in response to varying channel conditions.