Detection of a signal in colored noise: A random matrix theory based analysis
LD Chamain, P Dharmawansa, S Atapattu, C Tellambura
Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM) | IEEE | Published : 2020
This paper investigates the classical statistical signal processing problem of detecting a signal in the presence of colored noise with an unknown covariance matrix. In particular, we consider a scenario where m-dimensional p possible signal-plus-noise samples and m-dimensional n noise-only samples are available at the detector. Then the presence of a signal can be detected using the largest generalized eigenvalue (l.g.e.) of the so called whitened sample covariance matrix. This amounts to statistically characterizing the maximum eigenvalue of the deformed Jacobi unitary ensemble (JUE). To do this, we employ the powerful orthogonal polynomial approach to determine a new finite dimensional ex..View full abstract
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Awarded by Australian Research Council
This work is supported in part by the Australian Research Council (ARC) through the Discovery Early Career Researcher (DECRA) Award under Grant DE160100020.