Single Bit and Reduced Dimension Diffusion Strategies Over Distributed Networks

Abstract

We introduce novel diffusion based adaptive estimation strategies for distributed networks that have significantly less communication load and achieve comparable performance to the full information exchange configurations. After local estimates of the desired data is produced in each node, a single bit of information (or a reduced dimensional data vector) is generated using certain random projections of the local estimates. This newly generated data is diffused and then used in neighboring nodes to recover the original full information. We provide the complete state-space description and the mean stability analysis of our algorithms.

Submitted to the IEEE Signal Processing Letters

References in corpus (1)

• On the Influence of Informed Agents on Learning and Adaptation over Networks

Cited by in corpus (1)

• Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load