The Scientific World Journal

Volume 2013 (2013), Article ID 729839, 4 pages

http://dx.doi.org/10.1155/2013/729839

Research Article

The Exact Distribution of the Condition Number of Complex Random Matrices

Lin Shi,¹ Taibin Gan,² Hong Zhu,¹ and Xianming Gu²

¹School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China

²School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China

Received 19 August 2013; Accepted 24 September 2013

Academic Editors: E. A. Abdel-Salam and E. Francomano

Copyright © 2013 Lin Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Let be a complex random matrix and which is the complex Wishart matrix. Let and denote the eigenvalues of the W and singular values of , respectively. The 2-norm condition number of is . In this paper, the exact distribution of the condition number of the complex Wishart matrices is derived. The distribution is expressed in terms of complex zonal polynomials.

1. Introduction

Over the past decade, multiple-input and multiple-output (MIMO) systems have been at the forefront of wireless communications research and development, due to their huge potential for delivering significant capacity compared with conventional systems [1–13]. The capacity and performance of practical MIMO transmission schemes are often dictated by the statistical eigenproperties of the instantaneous channel correlation matrix , where is a complex Gaussian matrix and is known to follow a complex Wishart distribution.

In recent years, the statistical properties of Wishart matrices have been extensively studied and applied to a large number of MIMO applications. In statistics, the random eigenvalues are used in hypothesis testing, principal component analysis, canonical correlation analysis, multiple discriminant analysis, and so forth (see [7]). In nuclear physics, random eigenvalues are used to model nuclear energy levels and level spacing [6]. Moreover, the zeros of the Riemann zeta function are modeled using random eigenvalues [6]. Condition numbers arise in theory and applications of random matrices, such as multivariate statistics and quantum physics.

Let be a complex random matrix whose elements are independent and identically distributed (i.i.d.) standard normal random variables. As we know, the complex random matrix is a complex Wishart matrix. Its distribution is denoted by , . In addition, is a positive definite Hermitian matrix with real eigenvalues; let and denote the eigenvalues of the and singular values of , respectively. The 2-norm condition number of is ; thus, .

The exact distributions of the condition number of a matrix whose elements are independent and identically distributed standard normal real or complex random variables are given in [5] by Edelman. Edelman also obtained the limiting distributions and the limiting expected logarithms of the condition numbers of random rectangular matrices whose elements are independent and identically distributed standard normal random variables. The exact distributions of the condition number of Gaussian matrices are studied in [1] for real random matrix. Here, we derive the exact distribution of the condition number of a complex random matrix for special case .

This paper is arranged as follows. Section 2 gives some preliminary results to the complex random matrices. In Section 3, the main result of this work, the density function of for complex case, is proved.

2. Some Preliminary Results

In this section, we give some results on joint density of the eigenvalues of a complex Wishart matrix and , . The determinant, trace, and norm of a square matrix are denoted by , , and , respectively.

For any nonnegative integer , a portion of is a multiple , where such that , is the set of all portions of , and the symbol means the summation over ; that is, .

Let be any portion of , and let be the eigenvalues of an matrix as follows:

The zonal polynomials (also called Schur polynomials) of are defined by

The following is the basic properties of the zonal polynomials [8]:

The zonal polynomial of the identity matrix is defined by

where

Definition 1. For , where is positive definite Hermitian matrix with real eigenvalues, let be a complex random matrix whose elements are independent and identically distributed (i.i.d.) standard normal random variables. Let be the eigenvalues of and . If , with , then the joint density of its eigenvalues is defined [2] as follows:

where

Lemma 2. For an matrix , the product of two zonal polynomials can be expressed in terms of a weighted combination of another zonal polynomial [7]; that is, for all and , one has

where and is a constant coefficient.

Lemma 3. For an matrix and for all , one has [1]

where and is a constant coefficient.

Lemma 4. Let , and , then [2]

where

3. Main Result

The exact distribution of the 2-norm condition number of the Wishart matrix is derived by the following.

Theorem 5. Let be a complex random matrix and . and are the maximum and minimum eigenvalues of , . Then, the exact distribution of is given by

where .

Proof (consider (6)). By making the transformation , , , where , we obtain the joint distribution of and , as follows:

By using Taylor’s formula,

By using property (3), we have

where .

By making the transformation , , using Lemma 3, and integrating over the set , we have

where and .

By using Lemma 4, let be replaced by , let be replaced by , and let .

Then,

It follows that the distribution of is given by

Note that ; then, the distribution of is given by

4. Conclusions

In this paper, the exact distribution of the condition number of complex Wishart matrices is derived. The distribution is expressed in terms of complex zonal polynomials. This distribution plays an important role in numerical analysis and statistical hypothesis testing.

Acknowledgments

The authors would like to thank the anonymous reviewers for their detailed comments and suggestions. This work was supported by the Fund of Sichuan Provincial Key Laboratory of Signal and Information Processing, Xihua University (SZJJ2009-002).

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