Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection

We develop a resource-efficient framework for collaborative decision-making over distributed sensor networks by proposing a novel over-the-air soft information aggregation. We exploit the natural superposition of wireless transmissions to enable sensors to utilize over-the-air computation to approximate the sufficient statistic for optimum detection over a shared channel. By designing practical transmission and receiver processing in over-the-air computation, the decision-making fusion center can wirelessly obtain a good approximation of the aggregate log-likelihood ratio computed over all observed data with low distortion. Focusing on Neyman-Pearson tests for detection in this new framework, we develop efficient tests and analyze their performance bounds in several common joint detection scenarios. Our results show significant over-the-air collaboration gain even with a few participating sensors. The novel framework exhibits very little performance loss of detection accuracy against traditional multiple access transmission from sensing nodes despite substantial resource savings via over-the-air computation.