University of New Haven
In this paper, the authors consider estimating the spatial variations of a wireless channel based on a small number of measurements in a robotic network. They use a multi-scale probabilistic model in order to characterize the channel and develop an estimator based on this model. They show that their model-based approach can estimate the channel well for several scenarios, with only a small number of gathered measurements. They furthermore consider a sparsity-based channel estimation approach, in which they utilize the compressibility of the channel in the frequency domain. Their results show that this approach can also be effective in several scenarios.