Channel gain cartography via low rank and sparsity

Channel gain cartography aims at inferring the channel gains between arbitrary points in space based on measurements (samples) of channel gains taken from finite pairs of transceivers. Channel gain maps are useful for various sensing and resource allocation tasks, essential for the operation of cognitive radio networks. In this work, the channel gain samples are modeled as compressive tomographic measurements of an underlying spatial loss field (SLF), postulated to have low-rank structure corrupted by sparse errors. Efficient algorithms to reconstruct the SLF are developed, from which arbitrary channel gains can be interpolated.