Let $X_1 \cdots X_N$ are $N$ number of $m$ Dimensional Independent Complex Gaussian Random vectors Such that:
$$ X_j \sim \mathcal{N}(\mu,\Sigma)\; \forall \;j=1 \cdots N$$
Let $$X=\left[\begin{array}{ccc}X_1^T \\X_2^T \\ \vdots \\ X_N^T\end{array}\right]$$ be $N \times m$ matrix, Then by some algebra
$$ E(X)=\left[\begin{array}{ccc}\mu^T \\\mu^T \\ \vdots \\ \mu^T\end{array}\right]$$ and
$$ \operatorname{Cov}(\operatorname{Vec}(X^T))=I_n \otimes \Sigma $$ Then
$$ X \sim \mathcal{N}(\bf{1}\mu^T, I_n \otimes \Sigma) $$ Where
$$ \bf{1}=\left[\begin{array}{ccc} 1 \\1 \\ \vdots \\ 1\end{array}\right]$$ is Vector of all one's. Then
$$ W=X^H X $$ is $m \times m$ Wishart Random Matrix
$$ \mathbf{f(W)=|\Sigma|^{-\frac{n}{2}}\,|W|^{\frac{n-m-1}{2}}\,e^{-Tr(\frac{1}{2}\,\Sigma^{-1}\,W)}}$$
$$\Pr(W \leq xI)= \int_{0 \leq W \leq xI}\,f (W) \, dW $$ where $x$ is a scalar and $I$ is an $m \times m$ Identiy Matrix
Please give me any Hints..