On the Impact of Duty Cycle on the Estimation of Spatial Fields with Compressed Observations in M2M Capillary Networks
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. They assume that sensors operate with (independent) duty-cycles and they derive a closed-form expression of the optimal power allocation strategy which minimizes the estimation error for a given power constraint. For benchmarking purposes, they also assess the performance of another scheme based on a particularization of the partial KL transform.