Networking

Robust Estimation of Spatial Fields with Compressed Observations and Imperfect Phase Estimation in M2M Capillary Networks

Download Now Free registration required

Executive Summary

In this paper, the authors focus on the use capillary M2M (Machine-To-Machine) networks for the estimation of spatial random fields. The observations (samples) collected by the sensors are spatially correlated and, for this reason, they propose a distributed pre-coding scheme based on the Karhunen-Loeve (KL) transform. This allows the user to obtain an over-the-air compressed representation of such set of observations. In this paper, they derive a closed-form expression of the optimal power allocation strategy which is robust to residual phase synchronization errors and minimizes the estimation error for a given power constraint.

  • Format: PDF
  • Size: 186.45 KB