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Statistical Inference in Large Antenna Arrays under Unknown Noise Pattern

Authors
  • Vinogradova, Julia
  • Couillet, Romain
  • Hachem, Walid
Type
Published Article
Publication Date
Mar 05, 2014
Submission Date
Jan 02, 2013
Identifiers
DOI: 10.1109/TSP.2013.2280443
Source
arXiv
License
Yellow
External links

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.

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