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

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{{Header
 
{{Header
|Untermenü=Time variant transmission channels
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|Untermenü=Time-Variant Transmission Channels
|Vorherige Seite=Statistical bonds within the Rayleigh process
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|Vorherige Seite=Statistical Bonds Within the Rayleigh Process
 
|Nächste Seite=General description of time variant systems
 
|Nächste Seite=General description of time variant systems
 
}}
 
}}
 
== Channel model and Rice PDF ==
 
== Channel model and Rice PDF ==
 
<br>
 
<br>
The&nbsp; [[Mobile_Communication/Probability_density_of_Rayleigh_fading#A very general description of the mobile communication channel| 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.  
+
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.  
  
[[File:EN_Mob_T_1_4_S1.png|right|frame|Rice-Fading channel model|class=fit]]
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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; $y(t)$:
 
+
[[File:EN_Mob_T_1_4_S1.png|right|frame|Rice fading channel model|class=fit]]
If a direct component&nbsp; $($englisch:&nbsp; <i>Line of Sight</i>,&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; $y(t)$&nbsp; :
 
  
 
::<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>
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::<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 graphic shows this&nbsp; '''Rice&ndash;Fading&ndash;Channel model'''.&nbsp; As a special case, the Rayleigh&ndash;model results when &nbsp; $x_0 = y_0= 0$&nbsp;.<br>
+
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>
 
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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 Rice&ndash;Fading&ndash;model can be summarized as follows, see also&nbsp; [Hin08]<ref name = 'Hin08'>Hindelang, T.: ''Mobile Communications''. Vorlesungsmanuskript. Lehrstuhl für Nachrichtentechnik, TU München, 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 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>
*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>
 
  
*For&nbsp; $z_0 \ne 0$&nbsp; the value &nbsp; $|z(t)|$&nbsp; is [[Stochastic_Signal Theory/further_distributions#Riceversion| riceversified]], from which the term &bdquo;<i>Rice&ndash;Fading</i>&rdquo; is derived.  
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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.  
*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 <i>modified Bessel&ndash;function</i> of zero order:
+
*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:
  
::<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{mit}\hspace{0.5cm}{\rm I }_0 (u) = {\rm J }_0 ({\rm j} \cdot u) =  
+
::<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) =  
 
  \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>
  
*The greater the &bdquo;direct path power&rdquo;&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 communications channel
+
*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.
  
*If &nbsp; $|z_0| \gg \sigma$&nbsp; $($factor &nbsp;$3$&nbsp; or more$)$, the Rice&ndash;PDF can be approximated accurately by a Gaussian distribution with mean&nbsp; $|z_0|$&nbsp; and variance&nbsp; $\sigma$&nbsp; <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>
  
*In contrast to&nbsp; <i>Rayleigh fading</i> &nbsp; &rArr; &nbsp; $z_0 \equiv 0$, the phase at&nbsp; <i>Rice fading</i>&nbsp; is not equally distributed, but there is a preferred direction&nbsp; $\phi_0 = \arctan(y_0/x_0)$. Often one sets&nbsp; $y_0 = 0$ &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>
  
 
== Example of signal behaviour with Rice fading==
 
== Example of signal behaviour with Rice fading==
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:$${\rm E}\big [|z(t))|^2\big ] = 2 \cdot \sigma^2 = 1,$$
 
:$${\rm E}\big [|z(t))|^2\big ] = 2 \cdot \sigma^2 = 1,$$
  
*Rice fading&nbsp; (red curves)&nbsp; with the same&nbsp; $\sigma$&nbsp; and&nbsp
+
*Rice fading&nbsp; (red curves)&nbsp; with same&nbsp; $\sigma$&nbsp; and  
 
:$$x_0 = 0.707,\ \ y_0 = -0.707.$$
 
:$$x_0 = 0.707,\ \ y_0 = -0.707.$$
  
For the generation of the signal sections according to the above model, the&nbsp; [[Mobile_Communication/Statistical_Bonds_within_the_Rayleigh_process#Doppler_frequency_and_its_distribution|maximum_Doppler_frequency]]&nbsp; $f_\text{D, max} = 100 \ \rm Hz$&nbsp; was used as reference.  
+
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.  
  
The autocorrelation 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:
+
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>
  
It is taken into account that the spectral representation of a DC component leads to a Dirac function.<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>
It should be noted about this graphic:
+
It should be noted about this graph:
*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&nbsp; $f_x(x)$&nbsp; with variance&nbsp; $\sigma = 0.707$, either zero-mean (Rayleigh) or with mean&nbsp; $x_0$&nbsp; (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>
  
*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&nbsp; $f_y(y)$&nbsp; is thus a Gaussian curve with the variance&nbsp; $\sigma = 0. 707$&nbsp; around the mean value&nbsp; $ y_0 = -0.707$, thus axisymmetrical to the shown PDF&nbsp; $f_x(x)$.<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>
  
*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&nbsp; $f_a(a)$&nbsp;.  
+
*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 a lot of usable energy via the Rice direct path.
+
*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&nbsp; $f_\phi(\phi)$&nbsp; shows the preferred angle&nbsp; $\phi \approx 45^\circ$&nbsp; of the given Rice&ndash;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>
+
*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>
  
==Exercises zum Kapitel==
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==Exercises for the chapter==
 
<br>
 
<br>
 
[[Aufgaben:Exercise 1.6: Autocorrelation Function and PSD with Rice Fading]]
 
[[Aufgaben:Exercise 1.6: Autocorrelation Function and PSD with Rice Fading]]
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[[Aufgaben:Exercise 1.7: PDF of Rice Fading]]
 
[[Aufgaben:Exercise 1.7: PDF of Rice Fading]]
  
==List of sources==
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==References==
  
 
{{Display}}
 
{{Display}}

Latest revision as of 15:55, 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.