Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern

TL;DR

This paper develops new statistical eigen-inference methods for large antenna arrays operating under unknown noise patterns, enabling improved detection, estimation, and array processing techniques.

Contribution

It introduces novel eigen-inference methods for large antenna arrays with unknown noise, including a MUSIC-like algorithm for array processing.

Findings

Effective detection and estimation of multiple sources

Performance analysis of proposed techniques

Development of a MUSIC-like algorithm for unknown noise

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.