Towards spatially universal adaptive diffusion networks

Adaptive networks (ANs) rely on local adaptive filters (AFs) and a cooperation protocol to achieve a common goal, e.g., estimating a set of parameters. This protocol fuses the information from the rest of the network based on local combiners whose design impacts directly the network performance. Indeed, although diffusion schemes improve network performance on average, heterogeneity in signal statistics implies that indiscriminate cooperation may not be the best policy for good nodes. In this work, these observations lead to the introduction of different concepts of spatial universality which motivate a new adaptive combiner structure. The goal of the new combiner is to enforce that the cooperative AFs perform at least as well as the best individual non-cooperative AF, without discarding information from other nodes. The new structure has lower complexity and outperforms existing techniques, as illustrated by simulations. Network learning analysis is also provided.