Power-Spectral Density

Wiener-Khintchine Theorem

In the remainder of this paper we restrict ourselves to ergodic processes. As was shown in last chapter the following statements then hold:

Each individual sample function $x_i(t)$ is representative of the entire random process $\{x_i(t)\}$.
All time means are thus identical to the corresponding coulter means.
The autocorrelation function, which is generally affected by the two time parameters $t_1$ and $t_2$ now depends only on the time difference $τ = t_2 - t_1$ :

$$\varphi_x(t_1,t_2)={\rm E}\big[x(t_{\rm 1})\cdot x(t_{\rm 2})\big] = \varphi_x(\tau)= \int^{+\infty}_{-\infty}x(t)\cdot x(t+\tau)\,{\rm d}t.$$

The autocorrelation function provides quantitative information about the (linear) statistical bindings within the ergodic process $\{x_i(t)\}$ in the time domain. The equivalent descriptor in the frequency domain is the power spectral density , often referred to as the power density spectrum.

$\text{Definition:}$ The power spectral density (PSD) of an ergodic random process $\{x_i(t)\}$ is the Fourier transform of the auto-correlation function (ACF):

$${\it \Phi}_x(f)=\int^{+\infty}_{-\infty}\varphi_x(\tau) \cdot {\rm e}^{- {\rm j\hspace{0.05cm}\cdot \hspace{0.05cm} \pi}\hspace{0.05cm}\cdot \hspace{0.05cm} f \hspace{0.05cm}\cdot \hspace{0.05cm}\tau} {\rm d} \tau. $$

This functional is called the theorem of Wiener and Khinchin.

Similarly, the ACF can be computed as the inverse Fourier transform of the PSD (see page inverse Fourier transform in the book "Signal Representation"):

$$ \varphi_x(\tau)=\int^{+\infty}_{-\infty} {\it \Phi}_x \cdot {\rm e}^{- {\rm j\hspace{0.05cm}\cdot \hspace{0.05cm} \pi}\hspace{0.05cm}\cdot \hspace{0.05cm} f \hspace{0.05cm}\cdot \hspace{0.05cm}\tau} {\rm d} f.$$

The two equations are directly applicable only if the random process contains neither a DC component nor periodic components.
Otherwise, one must proceed according to the specifications given in page Power spectral density with DC component .

Physical interpretation and measurement

The following figure shows an arrangement for (approximate) metrological determination of the power density spectrum ${\it \Phi}_x(f)$.

To measure the power density spectrum

The following should be noted in this regard:

The random signal $x(t)$ is applied to a (preferably) rectangular and (preferably) narrowband filter with center frequency $f$ and bandwidth $Δf$ where $Δf$ must be chosen sufficiently small according to the desired frequency resolution.
The corresponding output signal $x_f(t)$ is squared and then the mean value is formed over a sufficiently long measurement period $T_{\rm M}$ . This gives the power of $x_f(t)$ or the power components of $x(t)$ in the spectral range from $f - Δf/2$ to $f + Δf/2$:

$$P_{x_f} =\overline{x_f(t)^2}=\frac{1}{T_{\rm M}}\cdot\int^{T_{\rm M}}_{0}x_f^2(t) \hspace{0.1cm}\rm d \it t.$$

Division by $Δf$ leads from spectral power to power spectral density:

$${{\it \Phi}_{x \rm +}}(f) =\frac{P_{x_f}}{{\rm \Delta} f} \hspace {0.5cm} {\rm bzw.} \hspace {0.5cm} {\it \Phi}_{x}(f) = \frac{P_{x_f}}{{\rm 2 \cdot \Delta} f}.$$

Here denotes ${\it \Phi}_{x+}(f) = 2 - {\it \Phi}_x(f)$ the one-sided PSD defined only for positive frequencies. For negative frequencies, ${\it \Phi}_{x+}(f) = 0$. In contrast, for the commonly used two-sided PSD : ${\it \Phi}_x(-f) = {\it \Phi}_x(f)$.

While the power $P_{x_f}$ tends to zero as the bandwidth $Δf$ becomes smaller, the power spectral density remains nearly constant above a sufficiently small value of $Δf$ For the exact determination of ${\it \Phi}_x(f)$ two boundary crossings are necessary:

$${{\it \Phi}_x(f)} = \lim_{{\rm \Delta}f\to 0} \hspace{0.2cm} \lim_{T_{\rm M}\to\infty}\hspace{0.2cm} \frac{1}{{\rm 2 \cdot \Delta}f\cdot T_{\rm M}}\cdot\int^{T_{\rm M}}_{0}x_f^2(t) \hspace{0.1cm} \rm d \it t.$$

$\text{Conclusion:}$

From this physical interpretation it further follows that the power spectral density is always real and can never become negative.
The total signal power of $x(t)$ is then obtained by integration over all spectral components:

$$P_x = \int^{\infty}_{0}{\it \Phi}_{x \rm +}(f) \hspace{0.1cm}{\rm d} f = \int^{+\infty}_{-\infty}{\it \Phi}_x(f)\hspace{0.1cm} {\rm d} f .$$

Reciprocity law of ACF duration and PSD bandwidth

All the Laws of Fourier Transform derived in the book "Signal Representation" for deterministic signals can also be applied to the auto-correlation function (ACF) and the Power Spectral Density (PSD) of a random process.

On the reciprocity law of ACF and PSD

Due to the specific properties

of autocorrelation function (always real and even).
and power density spectrum (always real, even, and non–negative)

however, do not all laws yield meaningful results.

We now consider, as in the section Interpretation of the Autocorrelation Function two different ergodic random processes $\{x_i(t)\}$ and $\{y_i(t)\}$ based on

of the two pattern signals $x(t)$ and $y(t)$ ⇒ upper sketch,
of the two autocorrelation functions $φ_x(τ)$ and $φ_y(τ)$ ⇒ middle sketch,
of the two power spectral densities ${\it \Phi}_x(f)$ and ${\it \Phi}_y(f)$ ⇒ bottom sketch.

Based on these graphs, the following statements can be made:

The areas under the PSD curves are equal ⇒ the processes $\{x_i(t)\}$ and $\{y_i(t)\}$ have equal power:

$${\varphi_x({\rm 0})}\hspace{0.05cm} =\hspace{0.05cm} \int^{+\infty}_{-\infty}{{\it \Phi}_x(f)} \hspace{0.1cm} {\rm d} f \hspace{0.2cm} = \hspace{0.2cm}{\varphi_y({\rm 0})} = \int^{+\infty}_{-\infty}{{\it \Phi}_y(f)} \hspace{0.1cm} {\rm d} f .$$

The well known from classical (deterministic) system theory Reciprocity Theorem of time duration and bandwidth also applies here:
A narrow autocorrelation function corresponds to a broad power density spectrum and vice versa.
As a descriptive quantity, we use here the equivalent PSD bandwidth $∇f$ $($man speaks "Nabla-f"$)$, similarly defined as the equivalent ACF duration $∇τ$ im Kapitel Interpretation of the auto-correlation function:

$${{\rm \nabla} f_x} = \frac {1}{{\it \Phi}_x(f = {\rm 0})} \cdot \int^{+\infty}_{-\infty}{{\it \Phi}_x(f)} \hspace{0.1cm} {\rm d} f, \hspace{0.5cm}{ {\rm \nabla} \tau_x} = \frac {\rm 1}{ \varphi_x(\tau = \rm 0)} \cdot \int^{+\infty}_{-\infty}{\varphi_x(\tau )} \hspace{0.1cm} {\rm d} \tau.$$

With these definitions, the following basic relationship holds:

$${{\rm \nabla} \tau_x} \cdot {{\rm \nabla} f_x} = 1\hspace{1cm}{\rm resp.}\hspace{1cm} {{\rm \nabla} \tau_y} \cdot {{\rm \nabla} f_y} = 1.$$

$\text{Example 1:}$ We start from the graph at the top of this page:

The characteristics of the higher frequency signal $x(t)$ are $∇τ_x = 0.33\hspace{0.08cm} \rm µs$ and $∇f_x = 3 \hspace{0.08cm} \rm MHz$.
The equivalent ACF duration of the signal $y(t)$ is three times: $∇τ_y = 1 \hspace{0.08cm} \rm µs$.
The equivalent PSD bandwidth is thus only more $∇f_y = ∇f_x/3 = 1 \hspace{0.08cm} \rm MHz$.

$\text{General:}$ The product of equivalent ACF duration ${ {\rm \nabla} \tau_x}$ and equivalent PSD bandwidth $ { {\rm \nabla} f_x}$ is always equal $1$:

$${ {\rm \nabla} \tau_x} \cdot { {\rm \nabla} f_x} = 1.$$

$\text{Proof:}$ According to the above definitions:

$${ {\rm \nabla} \tau_x} = \frac {\rm 1}{ \varphi_x(\tau = \rm 0)} \cdot \int^{+\infty}_{-\infty}{ \varphi_x(\tau )} \hspace{0.1cm} {\rm d} \tau = \frac { {\it \Phi}_x(f = {\rm 0)} }{ \varphi_x(\tau = \rm 0)},$$

$${ {\rm \nabla} f_x} = \frac {1}{ {\it \Phi}_x(f = {\rm0})} \cdot \int^{+\infty}_{-\infty}{ {\it \Phi}_x(f)} \hspace{0.1cm} {\rm d} f = \frac {\varphi_x(\tau = {\rm 0)} }{ {\it \Phi}_x(f = \rm 0)}.$$

Thus, the product is equal to $1$.

q.e.d.

$\text{Beispiel 2:}$ A limiting case of the reciprocity law represents the so-called white noise' :

This includes all spectral components (up to infinity).
The equivalent PSD bandwidth $∇f$ is infinite.

The law given here states that thus for the equivalent ACF duration $∇τ = 0$ must hold ⇒ the white noise has a dirac-shaped ACF.

For more on this topic, see the three-part tutorial video The AWGN channel, especially the second part

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Power spectral density with DC component

We assume an equal-signal-free random process $\{x_i(t)\}$ Further, we assume that the process also contains no periodic components. Then holds:

The autocorrelation function $φ_x(τ)$ vanishes for $τ → ∞$.
The power density spectrum ${\it \Phi}_x(f)$ - computable as the Fourier transform of $φ_x(τ)$ - is both continuous in value and continuous in time, i.e., without discrete components.

We now consider a second random process $\{y_i(t)\}$, which differs from the process $\{x_i(t)\}$ only by an additional DC component $m_y$ :

$$\left\{ y_i (t) \right\} = \left\{ x_i (t) + m_y \right\}.$$

The statistical descriptors of the mean-valued random process $\{y_i(t)\}$ then have the following properties:

The limit of the ACF for $τ → ∞$ is now no longer zero, but $m_y^2$. Throughout $τ$–the range from $-∞$ to $+∞$ the ACF $φ_y(τ)$ is larger by $m_y^2$ than $φ_x(τ)$:

$${\varphi_y ( \tau)} = {\varphi_x ( \tau)} + m_y^2 . $$

According to the elementary laws of the Fourier transform, the constant ACF contribution in the PSD leads to a Dirac function $δ(f)$ with weight $m_y^2$:

$${{\it \Phi}_y ( f)} = {\Phi_x ( f)} + m_y^2 \cdot \delta (f). $$

More detailed information about the Dirac function can be found in the chapter Gleichsignal - Grenzfall eines periodischen Signals (still in German) of the book "Signal Representation". Furthermore, we would like to refer you here to the learning video Herleitung und Visualisierung der Diracfunktion (still in German) .

Numerical PSD determination

Autocorrelation function and power density spectrum are strictly related via the Fourier transform This relationship also holds for discrete-time ACF representation with the sampling operator ${\rm A} \{ \varphi_x ( \tau ) \} $, thus for.

$${\rm A} \{ \varphi_x ( \tau ) \} = \varphi_x ( \tau ) \cdot \sum_{k= - \infty}^{\infty} T_{\rm A} \cdot \delta ( \tau - k \cdot T_{\rm A}).$$

The transition from the time– to the spectral domain can be derived with the following steps:

The distance $T_{\rm A}$ of two samples is determined by the absolute bandwidth $B_x$ (maximum occurring frequency within the process) via the sampling theorem:

$$T_{\rm A}\le\frac{1}{2B_x}.$$

The Fourier transform of the discrete-time (sampled) ACF yields an PSD periodic with ${\rm 1}/T_{\rm A}$ :

$${\rm A} \{ \varphi_x ( \tau ) \} \hspace{0.3cm} \circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.3cm} {\rm P} \{{{\it \Phi}_x} ( f) \} = \sum_{\mu = - \infty}^{\infty} {{\it \Phi}_x} ( f - \frac {\mu}{T_{\rm A}}).$$

$\text{Conclusion:}$ Since both $φ_x(τ)$ and ${\it \Phi}_x(f)$ are even and real functions, the relation holds:

$${\rm P} \{ { {\it \Phi}_x} ( f) \} = T_{\rm A} \cdot \varphi_x ( k = 0) +2 T_{\rm A} \cdot \sum_{k = 1}^{\infty} \varphi_x ( k T_{\rm A}) \cdot {\rm cos}(2{\rm \pi} f k T_{\rm A}).$$

The power density spectrum (PSD) of the continuous-time process is obtained from ${\rm P} \{ { {\it \Phi}_x} ( f) \}$ by bandlimiting to the range $\vert f \vert ≤ 1/(2T_{\rm A})$.
In the time domain, this operation means interpolating the individual ACF samples with the ${\rm si}$–function, where ${\rm si}(x)$ stands for $\sin(x)/x$ .

Discrete-time ACF and periodically continued PSD

$\text{Example 3:}$ A Gaussian ACF $φ_x(τ)$ is sampled at distance $T_{\rm A}$ where the sampling theorem is satisfied:

The Fourier transform of the discrete-time ACF ${\rm A} \{φ_x(τ) \}$ be ${\rm P} \{ { {\it \Phi}_x} ( f) \}$.
This with ${\rm 1}/T_{\rm A}$ periodic function ${\rm P} \{ { {\it \Phi}_x} ( f) \}$ is accordingly infinitely extended ( red curve ).
The PSD ${\it \Phi}_x(f)$ of the continuous-time process $\{x_i(t)\}$ is obtained by band-limiting to the frequency range highlighted in blue in the figure $\vert f - T_{\rm A} \vert ≤ 0.5$.

Accuracy of the numerical PSD calculation

For the following analysis, we make the following assumptions:

The discrete-time ACF $φ_x(k - T_{\rm A})$ was determined numerically from $N$ samples. As already shown on Accuracy of the numerical ACF calculation these values are in error and the errors are correlated if $N$ was chosen too small.
To calculate the periodic power density spectrum (PSD), we use only the ACF values $φ_x(0)$, ... , $φ_x(K - T_{\rm A})$:

$${\rm P} \{{{\it \Phi}_x} ( f) \} = T_{\rm A} \cdot \varphi_x ( k = 0) +2 T_{\rm A} \cdot \sum_{k = 1}^{K} \varphi_x ( k T_{\rm A})\cdot {\rm cos}(2{\rm \pi} f k T_{\rm A}).$$

$\text{Conclusion:}$ The accuracy of the PSD calculation is determined to a strong extent by the parameter $K$ :

If $K$ is chosen too small, the ACF values actually present $φ_x(k - T_{\rm A})$ with $k > K$ will not be taken into account.
If $K$ is too large, also such ACF values are considered, which should actually be zero and are finite only because of the numerical ACF calculation.
These values, however, are - due to a too small $N$ in the ACF calculation - only errors, and impair the PSD calculation more than they provide a useful contribution to the result.

Accuracy of numerical PSD calculation

$\text{Example 4:}$ We consider here a zero mean process with statistically independent samples.

Thus, only the ACF value $φ_x(0) = σ_x^2$ should be different from zero.
But if one determines the ACF numerically from only $N = 1000$ samples, one obtains finite ACF values even for $k ≠ 0$ .

The upper figure shows that these erroneous ACF–values can be up to $6\%$ of the maximum value.
The numerically determined power density spectrum is shown below. The theoretical curve is shown in yellow, which was calculated for $\vert f - T_{\rm A} \vert ≤ 0.5$ should be constant.
The green and purple curves illustrate how by $K = 3$ and $K = 10$ respectively, the result is distorted compared to $K = 0$ .

In this case (statistically independent random variables) the error grows monotonically with increasing $K$. In contrast, for a random variable with statistical bindings, there is an optimal value for $K$ in each case.

If this is chosen too small, significant bindings are not considered.
In contrast, a value that is too large leads to oscillations that can only be attributed to erroneous ACF values

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