Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern
Abstract
In this article, a general information-plus-noise transmission model is assumed, the receiver end of which is composed of a large number of sensors and is unaware of the noise pattern. For this model, and under reasonable assumptions, a set of results is provided for the receiver to perform statistical eigen-inference on the information part. In particular, we introduce new methods for the detection, counting, and the power and subspace estimation of multiple sources composing the information part of the transmission. The theoretical performance of some of these techniques is also discussed. An exemplary application of these methods to array processing is then studied in greater detail, leading in particular to a novel MUSIC-like algorithm assuming unknown noise covariance.
Cite
@article{arxiv.1301.0306,
title = {Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern},
author = {Julia Vinogradova and Romain Couillet and Walid Hachem},
journal= {arXiv preprint arXiv:1301.0306},
year = {2015}
}
Comments
25 pages, 5 figures