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Difference between revisions of "Aufgaben:Exercise 1.5: Reconstruction of the Jakes Spectrum"

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m (Text replacement - "power spectral density" to "power-spectral density")
 
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{{quiz-Header|Buchseite=Mobile Kommunikation/Statistische Bindungen innerhalb des Rayleigh-Prozesses}}
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{{quiz-Header|Buchseite=Mobile_Communications/Statistical_Bindings_within_the_Rayleigh_Process}}
  
 
[[File:P_ID2124__Mob_A_1_5.png|right|frame|Considered Jakes spectrum]]
 
[[File:P_ID2124__Mob_A_1_5.png|right|frame|Considered Jakes spectrum]]
In a mobile radio system, the  [[Mobile_Communications/Statistische_Bindungen_innerhalb_des_Rayleigh%E2%80%93Prozesses#Ph.C3.A4nomenologische_Beschreibung_des_Dopplereffekts|Doppler effect]]  is also noticeable in the power density spectrum of the Doppler frequency  fD .  
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In a mobile radio system, the  [[Mobile_Communications/Statistische_Bindungen_innerhalb_des_Rayleigh%E2%80%93Prozesses#Ph.C3.A4nomenologische_Beschreibung_des_Dopplereffekts|Doppler effect]]  is also noticeable in the power-spectral density of the Doppler frequency fD.  
  
This results in the so-called  [[Mobile_Communications/Statistical_bindings_within_the_Rayleigh_process#ACF_und_PSD_with_Rayleigh.E2.80.93Fading|Jakes spectrum]], which is shown in the graph for the maximum Doppler frequency  fD, max=100 Hz. Φz(fD)  has only portions within the range  ± f_{\rm D, \ max}, where
+
This results in the so-called  [[Mobile_Communications/Statistical_Bindings_within_the_Rayleigh_Process#ACF_and_PDS_with_Rayleigh.E2.80.93Fading|Jakes spectrum]], which is shown in the graph for the maximum Doppler frequency fD, max=100 Hz.  Φz(fD)  has only portions within the range  ± f_{\rm D, \ max}, where
:$${\it \Phi}_z(f_{\rm D}) = \frac{2 \cdot \sigma^2}{\pi \cdot f_{\rm D, \hspace{0.05cm} max}  \cdot \sqrt { 1 - (f_{\rm D}/f_{\rm D, \hspace{0.05cm} max})^2} }
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:$${\it \Phi}_z(f_{\rm D}) = \frac{2 \cdot \sigma^2}{\pi \cdot f_{\rm D, \hspace{0.1cm} max}  \cdot \sqrt { 1 - (f_{\rm D}/f_{\rm D, \hspace{0.1cm} max})^2} }
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
What is expressed in the frequency domain by the power spectral density (PSD) is described in the time domain by the autocorrelation function (ACF).  The ACF is the   Φz(fD)  by the  [[Signal_Representation/Fouriertransformation_und_-r%C3%BCcktransformation#Das_zweite_Fourierintegral|''' checkLink:_Buch_1 ⇒ ''' inverse Fourier transform]] of the PSD.
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What is expressed in the frequency domain by the power-spectral density  $\rm (PSD)$  is described in the time domain by the auto-correlation function  $\rm (ACF)$.  The ACF is the   Φz(fD)  by the  [[Signal_Representation/Fourier_Transform_and_Its_Inverse#The_Second_Fourier_Integral|inverse Fourier transform]]  of the PDS.
  
 
With the  [[Applets:Bessel_Functions_of_the_First_Kind|Bessel function]]  of the first kind and zero order  (J0)  you get
 
With the  [[Applets:Bessel_Functions_of_the_First_Kind|Bessel function]]  of the first kind and zero order  (J0)  you get
:$$\varphi_z ({\rm \Delta}t) = 2 \sigma^2 \cdot {\rm J_0}(2\pi \cdot f_{\rm D, \hspace{0.05cm} max} \cdot {\rm \Delta}t)\hspace{0.05cm}.$$
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:$$\varphi_z ({\rm \Delta}t) = 2 \sigma^2 \cdot {\rm J_0}(2\pi \cdot f_{\rm D, \hspace{0.1cm} max} \cdot {\rm \Delta}t)\hspace{0.05cm}.$$
  
To take into account the Doppler effect and thus a relative movement between transmitter and receiver in a system simulation, two digital filters are inserted in the  [[Mobile_Communications/Wahrscheinlichkeitsdichte_des_Rayleigh%E2%80%93Fadings#Modellierung_von_nichtfrequenzselektivem_Fading|Rayleigh channel model]], each with the frequency response  HDF(fD).  
+
To take into account the Doppler effect and thus a relative movement between transmitter and receiver in a system simulation, two digital filters are inserted in the  [[Mobile_Communications/Probability_Density_of_Rayleigh_Fading|Rayleigh channel model]], each with the frequency response  HDF(fD).  
  
 
The dimensioning of these filters is part of this task.
 
The dimensioning of these filters is part of this task.
 
*We restrict ourselves here to the branch for generating the real part  x(t).  The ratios derived here are also valid for the imaginary part  y(t).
 
*We restrict ourselves here to the branch for generating the real part  x(t).  The ratios derived here are also valid for the imaginary part  y(t).
*At the input of the left digital filter of the  [[Mobile_Communications/Wahrscheinlichkeitsdichte_des_Rayleigh%E2%80%93Fadings#Frequenzselektives_Fading_vs._nichtfrequenzselektives_Fading|Rayleigh channel model]] , there is white Gaussian noise  n(t)  with variance  σ2=0.5.  
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*At the input of the left digital filter of the  [[Mobile_Communications/Probability_Density_of_Rayleigh_Fading#Modeling_of_non-frequency_selective_fading|Rayleigh channel model]] , there is white Gaussian noise  n(t)  with variance  σ2=0.5.  
 
*The real component is then obtained from the  following convolution
 
*The real component is then obtained from the  following convolution
 
:x(t)=n(t)hDF(t).
 
:x(t)=n(t)hDF(t).
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''Notes:''  
 
''Notes:''  
* This task belongs to the topic of  [[Mobile_Communications/Statistical_bindings_within_the_Rayleigh_process|Statistical bindings within the Rayleigh process]].  
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* This task belongs to the topic of  [[Mobile_Communications/Statistical_Bindings_within_the_Rayleigh_Process|Statistical bindings within the Rayleigh process]].  
* The digital filter is treated in detail in chapter  [[Theory_of_Stochastic_Signals/Digitale_Filter|''' checkLink:_Buch_3 ⇒ ''' Digital Filter]]  of the book „Stochastic Signal Theory”.
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* The digital filter is treated in detail in chapter  [[Theory_of_Stochastic_Signals/Digitale_Filter|Digital Filter]]  of the book "Stochastic Signal Theory".
  
  
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<quiz display=simple>
 
<quiz display=simple>
{What is the value of the Jakes, spectrum of the real part at the Doppler frequency&nbsp; fD=0?
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{What is the value of the Jakes spectrum of the real part at the Doppler frequency fD=0?
 
|type="{}"}
 
|type="{}"}
Φx(fD=0) =  { 1.59 } $\ \cdot 10^{\rm &ndash;3} \ 1/{\rm Hz}$
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Φx(fD=0) =  { 1.59 } $\ \cdot 10^{\rm &ndash;3} \ {\rm Hz}^{-1}$
  
 
{Which dimensioning is correct, where &nbsp;K&nbsp; is an appropriately chosen constant?
 
{Which dimensioning is correct, where &nbsp;K&nbsp; is an appropriately chosen constant?
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+ The integral over&nbsp; |HDF(fD)|2&nbsp; must be&nbsp; 1&nbsp;.
 
+ The integral over&nbsp; |HDF(fD)|2&nbsp; must be&nbsp; 1&nbsp;.
  
{Is&nbsp; HDF(f)&nbsp; unambiguously defined by the two conditions according to '''(2)'' and '''(3)'''?
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{Is&nbsp; HDF(f)&nbsp; unambiguously defined by the two conditions according to&nbsp; '''(2)'''&nbsp; and&nbsp; '''(3)'''?
 
|type="()"}
 
|type="()"}
 
- Yes.
 
- Yes.
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</quiz>
 
</quiz>
  
===Solutions===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; The Jakes spectrum of the real part is half the resulting spectrum Φz(f):
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'''(1)'''&nbsp; The Jakes spectrum of the real part is half the resulting spectrum&nbsp; Φz(f):
 
:$${\it \Phi}_x(f_{\rm D} = 0) = {\it \Phi}_y(f_{\rm D} = 0) = \frac{{\it \Phi}_z(f_{\rm D} = 0)}{2}= \frac{\sigma^2}{\pi \cdot f_{\rm D, \hspace{0.05cm} max}} =  
 
:$${\it \Phi}_x(f_{\rm D} = 0) = {\it \Phi}_y(f_{\rm D} = 0) = \frac{{\it \Phi}_z(f_{\rm D} = 0)}{2}= \frac{\sigma^2}{\pi \cdot f_{\rm D, \hspace{0.05cm} max}} =  
 
  \frac{0.5}{\pi \cdot 100\,\,{\rm Hz}} \hspace{0.15cm} \underline{ = 1.59 \cdot 10^{-3}\,\,{\rm Hz^{-1}}}
 
  \frac{0.5}{\pi \cdot 100\,\,{\rm Hz}} \hspace{0.15cm} \underline{ = 1.59 \cdot 10^{-3}\,\,{\rm Hz^{-1}}}
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'''(2)'''&nbsp; <u>Solution 2</u> is correct:  
 
'''(2)'''&nbsp; <u>Solution 2</u> is correct:  
*The input signal n(t) has a white (constant) LDS Φn(fD).  
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*The input signal&nbsp; n(t)&nbsp; has a white (constant) PDS&nbsp; Φn(fD).  
*The PSD at the output is then
+
*The PDS at the output is then
$${\it \Phi}_x(f_{\rm D}) = {\it \Phi}_n(f_{\rm D}) \cdot | H_{\rm DF}(f_{\rm D}|^2
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:$${\it \Phi}_x(f_{\rm D}) = {\it \Phi}_n(f_{\rm D}) \cdot | H_{\rm DF}(f_{\rm D}|^2
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
  
 
'''(3)'''&nbsp; <u>Solution 3</u> is correct.  
 
'''(3)'''&nbsp; <u>Solution 3</u> is correct.  
*Only if this condition is fulfilled, the signal x(t) has the same variance σ2 as the noise signal n(t).
+
*Only if this condition is fulfilled, the signal&nbsp; x(t)&nbsp; has the same variance&nbsp; σ2&nbsp; as the noise signal&nbsp; n(t).
  
  
 
'''(4)'''&nbsp; <u>No</u>:  
 
'''(4)'''&nbsp; <u>No</u>:  
*The two conditions after subtasks (2) and (3) only refer to the magnitude of the digital filter.  
+
*The two conditions after subtasks&nbsp; '''(2)'''&nbsp; and&nbsp; '''(3)'''&nbsp; only refer to the magnitude of the digital filter.  
 
*There is no constraint for the phase of the digital filter.  
 
*There is no constraint for the phase of the digital filter.  
*This phase can be chosen arbitrarily. Usually it is chosen in such a way that a minimum phase network results.  
+
*This phase can be chosen arbitrarily.&nbsp; Usually it is chosen in such a way that a minimum phase network results.  
*In this case, the impulse response hDF(t) then has the lowest possible duration.
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*In this case, the impulse response&nbsp; hDF(t)&nbsp; then has the lowest possible duration.
  
  
The graph shows the result of the approximation. The red curves were determined simulatively over 100000 samples. You can see:
+
The graph shows the result of the approximation.&nbsp; The red curves were determined simulatively over 100000 samples.&nbsp; You can see:
[[File:EN_Mob_A_1_5d.png|right|frame|Approximation of the Jakes spectrum and ACF]]
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[[File:EN_Mob_A_1_5d.png|right|frame|Approximation of the Jakes spectrum and the auto-correlation function]]
  
 
+
* The Jakes PDS (left graph) can only be reproduced very inaccurately due to the vertical drop at&nbsp; &plusmn; f_{\rm D, \ max}.
* The Jakes power spectral density (left graph) can only be reproduced very inaccurately due to the vertical drop at &plusmn; f_{\rm D, \ max}.
+
* For the time domain, this means that the ACF decreases much faster than the theory suggests.  
* For the time domain, this means that the ACF decreases much faster than theory suggests.  
+
*For small values of&nbsp; $\Delta t$, however, the approximation is very good (right graph).
*For small values of  $\delta t$, however, the approximation is very good (right graph).
 
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Exercises for Mobile Communications|^1.3 Rayleigh Fading with Memory^]]
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[[Category:Mobile Communications: Exercises|^1.3 Rayleigh Fading with Memory^]]

Latest revision as of 13:41, 17 February 2022

Considered Jakes spectrum

In a mobile radio system, the  Doppler effect  is also noticeable in the power-spectral density of the Doppler frequency fD.

This results in the so-called  Jakes spectrum, which is shown in the graph for the maximum Doppler frequency fD, max=100 HzΦz(fD)  has only portions within the range  ±fD, max, where

Φz(fD)=2σ2πfD,max1(fD/fD,max)2.

What is expressed in the frequency domain by the power-spectral density  (PSD)  is described in the time domain by the auto-correlation function  (ACF).  The ACF is the   Φz(fD)  by the  inverse Fourier transform  of the PDS.

With the  Bessel function  of the first kind and zero order  (J0)  you get

φz(Δt)=2σ2J0(2πfD,maxΔt).

To take into account the Doppler effect and thus a relative movement between transmitter and receiver in a system simulation, two digital filters are inserted in the  Rayleigh channel model, each with the frequency response  HDF(fD).

The dimensioning of these filters is part of this task.

  • We restrict ourselves here to the branch for generating the real part  x(t).  The ratios derived here are also valid for the imaginary part  y(t).
  • At the input of the left digital filter of the  Rayleigh channel model , there is white Gaussian noise  n(t)  with variance  σ2=0.5.
  • The real component is then obtained from the following convolution
x(t)=n(t)hDF(t).


Notes:




Questions

1

What is the value of the Jakes spectrum of the real part at the Doppler frequency fD=0?

Φx(fD=0) = 

\ \cdot 10^{\rm –3} \ {\rm Hz}^{-1}

2

Which dimensioning is correct, where  K  is an appropriately chosen constant?

It holds  H_{\rm DF}(f_{\rm D}) = K \cdot {\it \Phi}_x(f_{\rm D}).
It applies  |H_{\rm DF}(f_{\rm D})|^2 = K \cdot {\it \Phi}_x(f_{\rm D})

3

From which condition can the constant  K  be determined?

K  can be selected as desired.
The integral over  |H_{\rm DF}(f_{\rm D})|  must equal  1 .
The integral over  |H_{\rm DF}(f_{\rm D})|^2  must be  1 .

4

Is  H_{\rm DF}(f)  unambiguously defined by the two conditions according to  (2)  and  (3)?

Yes.
No.


Solution

(1)  The Jakes spectrum of the real part is half the resulting spectrum  {\it \Phi}_z(f):

{\it \Phi}_x(f_{\rm D} = 0) = {\it \Phi}_y(f_{\rm D} = 0) = \frac{{\it \Phi}_z(f_{\rm D} = 0)}{2}= \frac{\sigma^2}{\pi \cdot f_{\rm D, \hspace{0.05cm} max}} = \frac{0.5}{\pi \cdot 100\,\,{\rm Hz}} \hspace{0.15cm} \underline{ = 1.59 \cdot 10^{-3}\,\,{\rm Hz^{-1}}} \hspace{0.05cm}.


(2)  Solution 2 is correct:

  • The input signal  n(t)  has a white (constant) PDS  {\it \Phi}_n(f_{\rm D}).
  • The PDS at the output is then
{\it \Phi}_x(f_{\rm D}) = {\it \Phi}_n(f_{\rm D}) \cdot | H_{\rm DF}(f_{\rm D}|^2 \hspace{0.05cm}.


(3)  Solution 3 is correct.

  • Only if this condition is fulfilled, the signal  x(t)  has the same variance  \sigma^2  as the noise signal  n(t).


(4)  No:

  • The two conditions after subtasks  (2)  and  (3)  only refer to the magnitude of the digital filter.
  • There is no constraint for the phase of the digital filter.
  • This phase can be chosen arbitrarily.  Usually it is chosen in such a way that a minimum phase network results.
  • In this case, the impulse response  h_{\rm DF}(t)  then has the lowest possible duration.


The graph shows the result of the approximation.  The red curves were determined simulatively over 100\hspace{0.05cm}000 samples.  You can see:

Approximation of the Jakes spectrum and the auto-correlation function
  • The Jakes PDS (left graph) can only be reproduced very inaccurately due to the vertical drop at  ± f_{\rm D, \ max}.
  • For the time domain, this means that the ACF decreases much faster than the theory suggests.
  • For small values of  \Delta t, however, the approximation is very good (right graph).