How to find the entropy of the circularly symmetric complex gaussian vector? I have a random complex column vector $\mathbb{x}$ of length $L$ which has circularly symmetric complex gaussian probability density function with mean $0$ and covariance matrix $\sigma^2 \mathbb{I}$ where $\mathbb{I}$ is the identity matrix of size $L$. I have read that for such random vector the differential entropy is given as $$H(\mathbb{x})=\log_2 \det(\pi e\sigma^2\mathbb{I}).$$ I the formula for finding the entropy is as follows $$H(\mathbb{x})=-\int_{-\infty}^{\infty}p(\mathbb{x})\log_2\left(p(\mathbb{x})\right)d\mathbb{x}.~~~~~~\text{Eq. 1}$$ Further, I know that $$p(\mathbb{x})=\frac{1}{\pi^L \det(\sigma^2\mathbb{I})}\exp(-\frac{\mathbb{\|x\|^2}}{\sigma^2}).~~~~~\text{Eq. 2}$$ When I put Eq. 2 into Eq. 1 I get $$H(\mathbb{x})=\frac{1}{\sigma^2 \pi^L \ln(2)}\left[\int_{-\infty}^{\infty}\ln(\pi^L \sigma^2)\exp(-\frac{\|\mathbb{x}\|^2}{\sigma^2})d\mathbb{x}+\frac{1}{\sigma^2 }\int_{-\infty}^{\infty}\|\mathbb{x}\|^2\exp(-\frac{\|\mathbb{x}\|^2}{\sigma^2})d\mathbb{x}\right].$$ How to proceed further to achieve $H(\mathbb{x})=\log_2\det(\pi e \sigma^2)$. Any help in this regard will be much appreciated. Thanks in advance.
 A: Formally, the entropy $H(X)$ of a complex random variable $X$ is defined as the entropy $H(\Re(X),\Im(X))$ of the (vector) random variable $[\Re(X),\Im(X)]$, consisting of the real and imaginary components of $X$. (This is in accordance to how the pdf of a complex variable is defined.) Now, for the case of $X$ being circularly symmetric Gaussian of zero mean and covariance $\sigma^2 \mathbf{I}$, its real and imaginary components are i.i.d., Gaussian of zero mean and variance $(\sigma^2/2)\mathbf{I}$. Therefore,
$$
\begin{align}
H(X) &= H(\Re(X),\Im(X)) \\
&= H(\Re(X)) + H(\Im(X))\\
&= \frac{1}{2} \log \det \left(2\pi e \frac{\sigma^2}{2} \mathbf{I} \right) + \frac{1}{2} \log \det \left(2\pi e \frac{\sigma^2}{2} \mathbf{I} \right)\\
&= \log \det \left(\pi e \sigma^2 \mathbf{I} \right)
\end{align}
$$
A: I am not sure if you have already gotten the answer. I am just posting my method. Hope it helps!

*

*First let us derive the entropy for a real random vector $\mathbf{Z}\in \mathbb{R}^{L\times1}$ which has multivariate gaussian pdf of mean $0$ and a generic covariance matrix $\mathbf{\Sigma}$ :

$\rightarrow$ Note that the pdf in this case ($\because \mu=0$) is given by :
$$f_{\mathbf{Z}}\left(z_{1}, \ldots, z_{L}\right)=\frac{\exp \left(-\frac{1}{2}\mathbf{z}^T\boldsymbol{\Sigma}^{-1}\mathbf{z}\right)}{\sqrt{(2 \pi)^{L}det(\boldsymbol{\Sigma})}}$$
$\rightarrow$ The differential entropy is given by :
$
\begin{align}
H(\mathbf{Z})&=-\int_{-\infty}^{\infty} f_\mathbf{Z}(\mathbf{z}) \log _{2}(f_\mathbf{Z}(\mathbf{z})) d \mathbf{z}\\
&= \frac{1}{2} \log _{2}\left((2 \pi)^{L} det(\boldsymbol{\Sigma})\right)\int_{-\infty}^{\infty} f_\mathbf{Z}(\mathbf{z}) d \mathbf{z}-\frac{1}{2} \log _{2}(e)\int_{-\infty}^{\infty} f_\mathbf{Z}(\mathbf{z})[\mathbf{z^T\Sigma^{-1} z}] d \mathbf{z}\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{1}{2} \log _{2}(e) \mathbb{E}\left[\mathbf{z}^T \mathbf{\Sigma}^{-1} \mathbf{z}\right] \\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{1}{2} \log _{2}(e)\mathbb{E}\left[tr\left(\mathbf{z}^T \mathbf{\Sigma}^{-1} \mathbf{z}\right)\right]\\
\text{[Using Trace Trick]}\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{1}{2} \log _{2}(e)\mathbb{E}\left[tr\left(\mathbf{\Sigma}^{-1}\mathbf{z}^T  \mathbf{z}\right)\right]\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{1}{2} \log _{2}(e)\cdot tr\left(\mathbf{\Sigma}^{-1}\mathbb{E}\left[\mathbf{z}^T  \mathbf{z}\right]\right)\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{1}{2} \log _{2}(e)\cdot tr\left(\mathbf{\Sigma}^{-1}\mathbf{\Sigma}\right)\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi \mathbf{\Sigma}\right)\right)+\frac{L}{2} \log _{2}(e)\\
&=\frac{1}{2} \log _{2}\left(det(\left(2 \pi e \mathbf{\Sigma}\right)\right)\\
\end{align}
$

*

*Now as @Stelios pointed out, we can write the following (where $\mathbf{X}$ is a complex random vector) :

\begin{aligned}
H(\mathbf{X}) &=H(\Re(\mathbf{X}), \mathfrak{I}(\mathbf{X})) \\
&=H(\Re(\mathbf{X}))+H(\mathfrak{I}(X)) \\
&=\frac{1}{2} \log\left(det\left(2 \pi e \frac{\mathbf{\Sigma}}{2}\right)\right)+\frac{1}{2} \log\left(det\left(2 \pi e \frac{\mathbf{\Sigma}}{2}\right)\right) \\
&= \log\left(det\left(\pi e \mathbf{\Sigma}\right)\right)
\end{aligned}

*

*For your specific case, where covariance matrix is $\sigma^2\mathbf{I}$, we get the required entropy expression by substituting this :

$$H(\mathbf{X}) = \log\left(det\left(\pi e \sigma^2\mathbf{I}\right)\right)$$
