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Cognitive Radio Communication and Networking: Principles and Practice by Robert Caiming Qiu, Michael C. Wicks, Zhen Hu, Husheng Li

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Chapter 3

Classical Detection

3.1 Formalism of Quantum Information

Fundamental Fact: The noise lies in a high-dimensional space; the signal, by contrast, lies in a much lower-dimensional space.

If a random matrix A has i.i.d rows Ai, then A*A = ∑iAiAiT where A* is the adjoint matrix of A. We often study A through the n × n symmetric, positive semidefinite matrix, the matrix A*A. The eigenvalues of images are therefore nonnegative real numbers.

An immediate application of random matrices is the fundamental problem of estimating covariance matrices of high-dimensional distributions [107]. The analysis of the row-independent models can be interpreted as a study of sample covariance matrices. For a general distribution in images, its covariance can be estimated from a sample size of N = O(nlogn) drawn from the distribution. For sub-Gaussian distributions, we have an even better bound N = O(n). For low-dimensional distributions, much fewer samples are needed: if a distribution lies close to a subspace of dimension r in images, then a sample of size N = O(rlogn) is sufficient for covariance estimation.

There are deep results in random matrix theory. The main motivation of this subsection is to exploit the existing ...

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