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

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[[File:EN_Mob_T_1_4_S1.png|right|frame|Rice-Fading channel model|class=fit]]
 
[[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; :
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If a direct component&nbsp; $(&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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*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; is [[Stochastische Signaltheorie/Weitere Verteilungen#Riceversion| riceversified]], from which the term &bdquo;<i>Rice&ndash;Fading</i>&rdquo; 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 <i>modified Bessel&ndash;function</i> of zero order:
  

Revision as of 20:37, 6 July 2020

Channel model and Rice PDF


The  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.

Rice-Fading channel model

If a direct component  $(  <i>Line of Sight</i>,  $\rm LoS)$  is present, it is necessary to add direct components   $x_0$  and/or  $y_0$  to the zero mean Gaussian processes   $x(t)$  and  $y(t)$  : :\[x(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} x(t) +x_0 \hspace{0.05cm}, \hspace{0.2cm} y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} y(t) +y_0\hspace{0.05cm},\] :\[z(t) = x(t) + {\rm j} \cdot y(t) \hspace{0.1cm} \Rightarrow \hspace{0.1cm} z(t) +z_0 \hspace{0.05cm},\hspace{0.2cm} z_0 = x_0 + {\rm j} \cdot y_0\hspace{0.05cm}.\] The graphic shows this  '''Rice–Fading–Channel model'''.  As a special case, the Rayleigh–model results when   $x_0 = y_0= 0$ .<br> The Rice–Fading–model can be summarized as follows, see also  [Hin08]'"`UNIQ--ref-00000008-QINU`"': *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  $x(t)$.<br> *For  $z_0 \ne 0$  the value   $|z(t)|$  is [[Stochastische Signaltheorie/Weitere Verteilungen#Riceversion| riceversified]], from which the term „<i>Rice–Fading</i>” 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 <i>modified Bessel–function</i> of zero order: :\[f_a(a) = \frac{a}{\sigma^2} \cdot {\rm exp} \big [ -\frac{a^2 + |z_0|^2}{2\sigma^2}\big ] \cdot {\rm I}_0 \left [ \frac{a \cdot |z_0|}{\sigma^2} \right ]\hspace{0.5cm}\text{mit}\hspace{0.5cm}{\rm I }_0 (u) = {\rm J }_0 ({\rm j} \cdot u) = \sum_{k = 0}^{\infty} \frac{ (u/2)^{2k}}{k! \cdot \Gamma (k+1)} \hspace{0.05cm}.\] *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 communications 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  $\sigma$  <br> *In contrast to  <i>Rayleigh fading</i>   ⇒   $z_0 \equiv 0$, the phase at  <i>Rice fading</i>  is not equally distributed, but there is a preferred direction  $\phi_0 = \arctan(y_0/x_0)$. Often one sets  $y_0 = 0$   ⇒   $\phi_0 = 0$.<br> =='"`UNIQ--h-1--QINU`"' Example of signal behaviour with Rice fading== <br> [[File:P ID2129 Mob T 1 4 S2 v1.png|right|frame|Comparison of Rayleigh fading (blue) and Rice fading (red)|class=fit]] The diagram shows typical signal characteristics and density functions of two mobile communication channels: *Rayleigh fading  (blue curves)  with  :'"`UNIQ-MathJax1-QINU`"' *Rice fading  (red curves)  with the same  $\sigma$  and  :'"`UNIQ-MathJax2-QINU`"' For the generation of the signal sections according to the above model, the  [[Mobile_Communication/Statistical_Bonds_within_the_Rayleigh_process#Doppler_frequency_and_its_distribution|maximum_Doppler_frequency]]  $f_\text{D, max} = 100 \ \rm Hz$  was used as reference. The autocorrelation 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 function.<br> <br clear="all"> It should be noted about this graphic: *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 variance  $\sigma = 0.707$, either zero-mean (Rayleigh) or with mean  $x_0$  (Rice).<br> *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 variance  $\sigma = 0. 707$  around the mean value  $ y_0 = -0.707$, thus axisymmetrical to the shown PDF  $f_x(x)$.<br> *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 a lot of 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 zum Kapitel


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

List of sources

  1. Hindelang, T.: Mobile Communications. Vorlesungsmanuskript. Lehrstuhl für Nachrichtentechnik, TU München, 2008.