Difference between revisions of "Mobile Communications/Non-Frequency-Selective Fading With Direct Component"

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{{Header
 
{{Header
|Untermenü=Zeitvariante Übertragungskanäle
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|Untermenü=Time-Variant Transmission Channels
|Vorherige Seite=Statistische Bindungen innerhalb des Rayleigh–Prozesses
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|Vorherige Seite=Statistical Bonds Within the Rayleigh Process
|Nächste Seite=Allgemeine Beschreibung zeitvarianter Systeme
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|Nächste Seite=General description of time variant systems
 
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== Channel model and Rice PDF ==
 +
<br>
 +
The&nbsp; [[Mobile_Communications/Probability_Density_of_Rayleigh_Fading#Modeling_of_non-frequency_selective_fading| $\text{Rayleigh distribution}$]]&nbsp; describes the mobile communication channel under the assumption that there is no direct path and thus the multiplicative factor&nbsp; $z(t) = x(t) + {\rm j} \cdot y(t)$&nbsp; is solely composed of diffusely scattered components.
  
== Kanalmodell und Rice–WDF ==
+
If a direct component&nbsp; $($Line of Sight,&nbsp; $\rm LoS)$&nbsp; is present, it is necessary to add direct components &nbsp; $x_0$&nbsp; and/or&nbsp; $y_0$&nbsp; to the zero mean Gaussian processes &nbsp; $x(t)$&nbsp; and&nbsp; $y(t)$:
<br>
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[[File:EN_Mob_T_1_4_S1.png|right|frame|Rice fading channel model|class=fit]]
Die [[Mobile_Kommunikation/Wahrscheinlichkeitsdichte_des_Rayleigh%E2%80%93Fadings#Allgemeine_Beschreibung_des_Mobilfunkkanals| Rayleigh&ndash;Verteilung]] beschreibt den Mobilfunkkanal unter der Annahme, dass kein direkter Pfad vorhanden ist und sich somit der multiplikative Faktor $z(t) = x(t) + {\rm j} \cdot y(t)$ allein aus diffus gestreuten Komponenten zusammensetzt. Bei Vorhandensein einer Direktkomponente (englisch: <i>Line of Sight</i>, LoS) muss man im Modell zu den mittelwertfreien Gaußprozessen $x(t)$ und $y(t)$ noch Gleichkomponenten hinzufügen:
 
  
 
::<math>x(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} x(t) +x_0 \hspace{0.05cm}, \hspace{0.2cm} y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} y(t) +y_0\hspace{0.05cm},</math>
 
::<math>x(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} x(t) +x_0 \hspace{0.05cm}, \hspace{0.2cm} y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} y(t) +y_0\hspace{0.05cm},</math>
Line 14: Line 16:
 
::<math>z(t) = x(t) + {\rm j} \cdot y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} z(t) +z_0 \hspace{0.05cm},\hspace{0.2cm}
 
::<math>z(t) = x(t) + {\rm j} \cdot y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} z(t) +z_0 \hspace{0.05cm},\hspace{0.2cm}
 
  z_0 = x_0 + {\rm j} \cdot y_0\hspace{0.05cm}.</math>
 
  z_0 = x_0 + {\rm j} \cdot y_0\hspace{0.05cm}.</math>
 +
The graph shows this&nbsp; &raquo;'''Rice fading channel model'''&laquo;.&nbsp; As a special case, the Rayleigh model results when &nbsp; $x_0 = y_0= 0$.
 +
<br clear=all>
 +
The Rice fading model can be summarized as follows, see also&nbsp; [Hin08]<ref name = 'Hin08'>Hindelang, T.:&nbsp; Mobile Communications. &nbsp; Lecture notes. Institute for Communications Engineering. &nbsp; Technical University of Munich, 2008.</ref>:
 +
*The real part&nbsp; $x(t)$&nbsp; is Gaussian distributed with mean value&nbsp; $x_0$&nbsp; and variance&nbsp; $\sigma ^2$.
 +
*The imaginary part&nbsp; $y(t)$&nbsp; is also Gaussian distributed&nbsp; $($mean&nbsp; $y_0$,&nbsp; equal variance&nbsp; $\sigma ^2)$&nbsp; and independent of&nbsp; $x(t)$.<br>
  
Die Grafik zeigt diess '''Rice&ndash;Fading&ndash;Kanalmodell'''. Als Sonderfall ergibt sich daraus wieder  das Rayleigh&ndash;Modell, wenn man $x_0 =  y_0= 0$ setzt.<br>
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*For&nbsp; $z_0 \ne 0$&nbsp; the value&nbsp; $|z(t)|$&nbsp; has a [[Theory_of_Stochastic_Signals/Further_distributions#Rice_PDF|$\text{Rice PDF}$]], from which the term&nbsp; "Rice fading"&nbsp; is derived.
 
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*To simplify the notation we set&nbsp; $|z(t)| = a(t)$. &nbsp; For&nbsp; $a < 0$&nbsp; it's PDF is&nbsp; $f_a(a) \equiv 0$,&nbsp; for&nbsp; $a \ge 0$ the following equation applies, where&nbsp; $\rm I_0(\cdot)$&nbsp; denotes the&nbsp; "modified Bessel&ndash;function" of zero order:
[[File:P ID2126 Mob T 1 4 S1 v3.png|center|frame|Rice-Fading-Kanalmodell|class=fit]]
 
 
 
Das Rice&ndash;Fading&ndash;Modell lässt sich wie folgt zusammenfassen:
 
*Der Realteil $x(t)$ ist gaußverteilt (Mittelwert $x_0$, Varianz $\sigma ^2$). Der Imaginärteil $y(t)$ ist ebenfalls gaußverteilt  (Mittelwert $y_0$, Varianz $\sigma ^2$)  sowie unabhängig von $x(t)$.<br>
 
  
*Für $z_0 \ne 0$ ist der Betrag $|z(t)|$ [[Stochastische_Signaltheorie/Weitere_Verteilungen#Riceverteilung| riceverteilt]], woraus die Bezeichnung &bdquo;<i>Rice&ndash;Fading</i>&rdquo; herrührt.
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::<math>f_a(a) = \frac{a}{\sigma^2} \cdot {\rm exp} \big [ -\frac{a^2 + |z_0|^2}{2\sigma^2}\big ] \cdot {\rm I}_0 \left [ \frac{a \cdot |z_0|}{\sigma^2} \right ]\hspace{0.5cm}\text{with}\hspace{0.5cm}{\rm I }_0 (u) = {\rm J }_0 ({\rm j} \cdot u) =  
*Zur Vereinfachung der Schreibweise setzen wir  $|z(t)| = a(t)$. Für $a < 0$ ist die Betrags&ndash;WDF $f_a(a) \equiv 0$, für $a \ge  0$ gilt folgende Gleichung ($I_0$ bezeichnetdie <i>modifizierte Bessel&ndash;Funktion</i> nullter Ordnung):
 
 
 
::<math>f_a(a) = \frac{a}{\sigma^2} \cdot {\rm exp} [ -\frac{a^2 + |z_0|^2}{2\sigma^2}] \cdot {\rm I}_0 \left [ \frac{a \cdot |z_0|}{\sigma^2} \right ]\hspace{0.5cm}\text{mit}\hspace{0.5cm}{\rm I }_0 (u) = {\rm J }_0 ({\rm j} \cdot u) =  
 
 
  \sum_{k = 0}^{\infty} \frac{ (u/2)^{2k}}{k! \cdot \Gamma (k+1)}
 
  \sum_{k = 0}^{\infty} \frac{ (u/2)^{2k}}{k! \cdot \Gamma (k+1)}
 
  \hspace{0.05cm}.</math>
 
  \hspace{0.05cm}.</math>
  
*Der Mobilfunkkanal ist um so besser für die Digitalsignalübertragung geeignet, je größer die &bdquo;Direktpfadleistung&rdquo;  $(|z_0|^2)$ gegenüber den Leistungen der Streukomponenten $2\sigma^2)$ ist.<br>
+
*The greater the direct path power&nbsp; $(|z_0|^2)$&nbsp; compared to the power of the stray components&nbsp; $(2\sigma^2)$&nbsp; the better suited for digital signal transmission is the mobile communication channel.
  
*Ist $|z_0| \gg \sigma$ (Faktor 3 oder mehr), so  kann die Rice&ndash;WDF mit guter Näherung durch eine Gaußverteilung mit dem Mittelwert $|z_0|$ und der Streuung $\sigma$ angenähert werden.<br>
+
*If &nbsp; $|z_0| \gg \sigma$&nbsp; $($factor &nbsp;$3$&nbsp; or more$)$, the Rice PDF can be approximated accurately by a Gaussian distribution with mean&nbsp; $|z_0|$&nbsp; and variance&nbsp; $\sigma^2$. <br>
  
*Im Gegensatz zum <i>Rayleigh&ndash;Fading</i> &nbsp; &rArr; &nbsp; $z_0 \equiv 0$ ist die Phase bei <i>Rice&ndash;Fading</i> nicht gleichverteilt, sondern es gibt eine Vorzugsrichtung $\phi_0 = \arctan(y_0/x_0)$. Oft setzt man $y_0 = 0$ &nbsp; &#8658; &nbsp; $\phi_0 = 0$.<br>
+
*In contrast to&nbsp; Rayleigh fading &nbsp; &rArr; &nbsp; $z_0 \equiv 0$, the phase at&nbsp; Rice fading&nbsp; is not equally distributed, but there is a preferred direction&nbsp; $\phi_0 = \arctan(y_0/x_0)$.&nbsp; Often one sets&nbsp; $y_0 = 0$ &nbsp; &#8658; &nbsp; $\phi_0 = 0$.<br>
  
== Beispielhafte Signalverläufe bei Rice–Fading==
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== Example of signal behaviour with Rice fading==
 
<br>
 
<br>
Die Grafik zeigt Signalverläufe und Dichtefunktionen zweier Mobilfunkkanäle:
+
[[File:P ID2129 Mob T 1 4 S2 v1.png|right|frame|Comparison of Rayleigh fading (blue) and Rice fading (red)|class=fit]]
[[File:P ID2129 Mob T 1 4 S2 v1.png|right|frame|Vergleich von Rayleigh-Fading (blau) und Rice-Fading (rot)|class=fit]]
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The diagram shows typical signal characteristics and density functions of two mobile communication channels:
*Rayleigh&ndash;Fading mit ${\rm E}[|z(t))|^2] = 2 \cdot \sigma^2 = 1$ (blaue Kurven),<br>
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*Rayleigh fading&nbsp; (blue curves)&nbsp; with&nbsp;  
 +
:$${\rm E}\big [|z(t))|^2\big ] = 2 \cdot \sigma^2 = 1,$$
  
*Rice&ndash;Fading mit gleichem $\sigma$ sowie $x_0 = 0.707$ und $y_0 = -0.707$ (rote Kurven).<br><br>
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*Rice fading&nbsp; (red curves)&nbsp; with same&nbsp; $\sigma$&nbsp; and
 +
:$$x_0 = 0.707,\ \ y_0 = -0.707.$$
  
Für die Erzeugung der Signalausschnitte mit dem auf der letzten Seite gezeigten Modell wurde in beiden Fällen die [[Mobile_Kommunikation/Statistische_Bindungen_innerhalb_des_Rayleigh%E2%80%93Prozesses#Dopplerfrequenz_und_deren_Verteilung| maximale Dopplerfrequenz]] $f_\text{D, max} = 100 \ \rm Hz$ zugrundegelegt.  
+
For the generation of the signals according to the above model, the&nbsp; [[Mobile_Communications/Statistical_Bindings_within_the_Rayleigh_Process#Doppler_frequency_and_its_distribution|$\text{maximum Doppler frequency}$]]&nbsp; $f_\text{D, max} = 100 \ \rm Hz$&nbsp; was used as reference.  
  
AKF und LDS von Rayleigh&ndash; und Rice&ndash;Fading unterscheiden sich nur geringfügig. Es gilt:
+
The auto-correlation function&nbsp; $\rm (ACF)$&nbsp; and power-spectral density&nbsp; $\rm (PSD)$&nbsp; of Rayleigh and Rice differ only slightly, other than adjusted parameter values.&nbsp; The following applies:
  
 
::<math>\varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rice}}  \hspace{-0.5cm}  =  \varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \hspace{0.05cm},</math>
 
::<math>\varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rice}}  \hspace{-0.5cm}  =  \varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \hspace{0.05cm},</math>
 
::<math> {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rice}} \hspace{-0.5cm}  =    {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \cdot \delta (f_{\rm D}) \hspace{0.05cm}.</math>
 
::<math> {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rice}} \hspace{-0.5cm}  =    {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \cdot \delta (f_{\rm D}) \hspace{0.05cm}.</math>
  
Berücksichtigt ist, dass die Spektraldarstellung eines  Gleichanteils zu einer Diracfunktion führt.<br>
+
It is taken into account that the spectral representation of a DC component leads to a Dirac delta function.<br>
 
 
 
<br clear= all>
 
<br clear= all>
Zu dieser Grafik ist anzumerken:
+
It should be noted about this graph:
*Die Realteile $x(t)$ von Rayleigh (blau), Rice (rot) unterscheiden sich durch die Konstante $x_0 = 0.707$. Die statistischen Eigenschaften sind ansonsten gleich: Gaußsche WDF $f_x(x)$ mit Streuung $\sigma = 0.707$, entweder mittelwertfrei (Rayleigh) oder mit Mittelwert $x_0$ (Rice).<br>
+
*The real parts&nbsp; $x(t)$&nbsp; of Rayleigh (blue) and Rice (red) only differ by the constant&nbsp; $x_0 = 0.707$. &nbsp; The statistical properties are otherwise the same: &nbsp; Gaussian PDF $f_x(x)$&nbsp; with standard deviation&nbsp; $\sigma = 0.707$, either zero-mean (Rayleigh) or with mean&nbsp; $x_0$&nbsp; (Rice).<br>
  
*Im Imaginärteil $y(t)$ erkennt man bei Rice zusätzlich die Gleichkomponente $y_0 = -0.707$. Die (in der Grafik nicht dargestellte) WDF $f_y(y)$ ist somit eine Gaußkurve mit der Streuung $\sigma = 0.707$ um den Mittelwert$ y_0 = -0.707$, also achsensymmetrisch zur skizzierten WDF $f_x(x)$.<br>
+
*In the imaginary part&nbsp; $y(t)$&nbsp; of the Rice distribution one can additionally recognize the direct component&nbsp; $y_0 = -0.707$.&nbsp; The (here not shown) PDF $f_y(y)$&nbsp; is thus a Gaussian curve with the standard deviation&nbsp; $\sigma = 0. 707$&nbsp; around the mean value&nbsp; $ y_0 = -0.707$, thus axisymmetrical to the shown PDF $f_x(x)$.<br>
  
*Die (logarithmische) Betragsdarstellung &nbsp; &#8658; &nbsp; $a(t) =|z(t)|$   zeigt, dass die rote Kurve meist oberhalb der blauen liegt. Dies wird auch aus der WDF deutlich. Beim Rice&ndash;Kanal ist die Fehlerwahrscheinlichkeit unter Berücksichtigung von AWGN&ndash;Rauschen niedriger als bei Rayleigh, da der Empfänger über den Rice&ndash;Direktpfad viel nutzbare Energie erhält.<br>
+
*The (logarithmic) representation of &nbsp; &#8658; &nbsp; $a(t) =|z(t)|$ shows that the red curve is usually above the blue one.&nbsp; This can also be read from the PDF $f_a(a)$&nbsp;.
 +
*For the Rice channel, the error probability is lower than for Rayleigh when AWGN is taken into account, since the receiver gets some usable energy via the Rice direct path.
  
*Die WDF $f_\phi(\phi)$ zeigt den Vorzugswinkel  $\phi \approx 45^\circ$ des Rice&ndash;Kanals. Der komplexe Faktor $z(t)$ befindet sich  wegen $x_0 > 0$ und $y_0 < 0$ großteils im vierten Quadranten, während beim Rayleigh&ndash;Kanal alle Quadranten gleichwahrscheinlich sind.<br>
+
*The PDF $f_\phi(\phi)$&nbsp; shows the preferred angle&nbsp; $\phi \approx -45^\circ$&nbsp; of the given Rice channel &nbsp; The complex factor&nbsp; $z(t)$&nbsp; is located mainly in the fourth quadrant because of&nbsp; $x_0 > 0$&nbsp; and&nbsp; $y_0 < 0$&nbsp;, whereas in the Rayleigh channel all quadrants are equally probable.<br>
  
==Aufgaben zum Kapitel==
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==Exercises for the chapter==
 
<br>
 
<br>
[[Aufgaben:1.6 Rice–Fading – AKF/LDS|A1.6 Rice–Fading – AKF/LDS]]
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[[Aufgaben:Exercise 1.6: Autocorrelation Function and PSD with Rice Fading]]
  
[[Aufgabe_1.6Z:_Rayleigh_und_Rice_im_Vergleich|Zusatzaufgaben:1.6 Rayleigh und Rice im Vergleich]]
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[[Aufgaben:Exercise 1.6Z: Comparison of Rayleigh and Rice]]
  
[[Aufgaben:1.7 WDF des Rice–Fadings|A1.7 WDF des Rice–Fadings]]
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[[Aufgaben:Exercise 1.7: PDF of Rice Fading]]
  
==Quellenverzeichnis==
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==References==
  
 
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Latest revision as of 16:55, 3 February 2023

Channel model and Rice PDF


The  $\text{Rayleigh distribution}$  describes the mobile communication channel under the assumption that there is no direct path and thus the multiplicative factor  $z(t) = x(t) + {\rm j} \cdot y(t)$  is solely composed of diffusely scattered components.

If a direct component  $($Line of Sight,  $\rm LoS)$  is present, it is necessary to add direct components   $x_0$  and/or  $y_0$  to the zero mean Gaussian processes   $x(t)$  and  $y(t)$:

Rice fading channel model
\[x(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} x(t) +x_0 \hspace{0.05cm}, \hspace{0.2cm} y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} y(t) +y_0\hspace{0.05cm},\]
\[z(t) = x(t) + {\rm j} \cdot y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} z(t) +z_0 \hspace{0.05cm},\hspace{0.2cm} z_0 = x_0 + {\rm j} \cdot y_0\hspace{0.05cm}.\]

The graph shows this  »Rice fading channel model«.  As a special case, the Rayleigh model results when   $x_0 = y_0= 0$.
The Rice fading model can be summarized as follows, see also  [Hin08][1]:

  • The real part  $x(t)$  is Gaussian distributed with mean value  $x_0$  and variance  $\sigma ^2$.
  • The imaginary part  $y(t)$  is also Gaussian distributed  $($mean  $y_0$,  equal variance  $\sigma ^2)$  and independent of  $x(t)$.
  • For  $z_0 \ne 0$  the value  $|z(t)|$  has a $\text{Rice PDF}$, from which the term  "Rice fading"  is derived.
  • To simplify the notation we set  $|z(t)| = a(t)$.   For  $a < 0$  it's PDF is  $f_a(a) \equiv 0$,  for  $a \ge 0$ the following equation applies, where  $\rm I_0(\cdot)$  denotes the  "modified Bessel–function" of zero order:
\[f_a(a) = \frac{a}{\sigma^2} \cdot {\rm exp} \big [ -\frac{a^2 + |z_0|^2}{2\sigma^2}\big ] \cdot {\rm I}_0 \left [ \frac{a \cdot |z_0|}{\sigma^2} \right ]\hspace{0.5cm}\text{with}\hspace{0.5cm}{\rm I }_0 (u) = {\rm J }_0 ({\rm j} \cdot u) = \sum_{k = 0}^{\infty} \frac{ (u/2)^{2k}}{k! \cdot \Gamma (k+1)} \hspace{0.05cm}.\]
  • The greater the direct path power  $(|z_0|^2)$  compared to the power of the stray components  $(2\sigma^2)$  the better suited for digital signal transmission is the mobile communication channel.
  • If   $|z_0| \gg \sigma$  $($factor  $3$  or more$)$, the Rice PDF can be approximated accurately by a Gaussian distribution with mean  $|z_0|$  and variance  $\sigma^2$.
  • In contrast to  Rayleigh fading   ⇒   $z_0 \equiv 0$, the phase at  Rice fading  is not equally distributed, but there is a preferred direction  $\phi_0 = \arctan(y_0/x_0)$.  Often one sets  $y_0 = 0$   ⇒   $\phi_0 = 0$.

Example of signal behaviour with Rice fading


Comparison of Rayleigh fading (blue) and Rice fading (red)

The diagram shows typical signal characteristics and density functions of two mobile communication channels:

  • Rayleigh fading  (blue curves)  with 
$${\rm E}\big [|z(t))|^2\big ] = 2 \cdot \sigma^2 = 1,$$
  • Rice fading  (red curves)  with same  $\sigma$  and
$$x_0 = 0.707,\ \ y_0 = -0.707.$$

For the generation of the signals according to the above model, the  $\text{maximum Doppler frequency}$  $f_\text{D, max} = 100 \ \rm Hz$  was used as reference.

The auto-correlation function  $\rm (ACF)$  and power-spectral density  $\rm (PSD)$  of Rayleigh and Rice differ only slightly, other than adjusted parameter values.  The following applies:

\[\varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rice}} \hspace{-0.5cm} = \varphi_z ({\rm \Delta}t)\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \hspace{0.05cm},\]
\[ {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rice}} \hspace{-0.5cm} = {\it \Phi}_z(f_{\rm D})\Bigg |_{\hspace{0.1cm}{\rm Rayleigh}} \hspace{-0.8cm} + |z_0|^2 \cdot \delta (f_{\rm D}) \hspace{0.05cm}.\]

It is taken into account that the spectral representation of a DC component leads to a Dirac delta function.

It should be noted about this graph:

  • The real parts  $x(t)$  of Rayleigh (blue) and Rice (red) only differ by the constant  $x_0 = 0.707$.   The statistical properties are otherwise the same:   Gaussian PDF $f_x(x)$  with standard deviation  $\sigma = 0.707$, either zero-mean (Rayleigh) or with mean  $x_0$  (Rice).
  • In the imaginary part  $y(t)$  of the Rice distribution one can additionally recognize the direct component  $y_0 = -0.707$.  The (here not shown) PDF $f_y(y)$  is thus a Gaussian curve with the standard deviation  $\sigma = 0. 707$  around the mean value  $ y_0 = -0.707$, thus axisymmetrical to the shown PDF $f_x(x)$.
  • The (logarithmic) representation of   ⇒   $a(t) =|z(t)|$ shows that the red curve is usually above the blue one.  This can also be read from the PDF $f_a(a)$ .
  • For the Rice channel, the error probability is lower than for Rayleigh when AWGN is taken into account, since the receiver gets some usable energy via the Rice direct path.
  • The PDF $f_\phi(\phi)$  shows the preferred angle  $\phi \approx -45^\circ$  of the given Rice channel   The complex factor  $z(t)$  is located mainly in the fourth quadrant because of  $x_0 > 0$  and  $y_0 < 0$ , whereas in the Rayleigh channel all quadrants are equally probable.

Exercises for the chapter


Exercise 1.6: Autocorrelation Function and PSD with Rice Fading

Exercise 1.6Z: Comparison of Rayleigh and Rice

Exercise 1.7: PDF of Rice Fading

References

  1. Hindelang, T.:  Mobile Communications.   Lecture notes. Institute for Communications Engineering.   Technical University of Munich, 2008.