Statistical Inference in Large Antenna Arrays Under Unknown Noise Pattern

In this paper, 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 correlation pattern. For this model, under an isotropy assumption between signal and noise left- and right-eigenspaces, 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 with unknown time correlated noise is then studied in greater detail, leading to a novel MUSIC-like algorithm.