Difference between revisions of "Aufgaben:Exercise 1.5: Reconstruction of the Jakes Spectrum"
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*The input signal n(t) has a white (constant) LDS Φn(fD). | *The input signal n(t) has a white (constant) LDS Φn(fD). | ||
*The PSD at the output is then | *The PSD at the output is then | ||
− | $${\it \Phi}_x(f_{\rm D}) = {\it \Phi}_n(f_{\rm D}) \cdot | H_{\rm DF}(f_{\rm D}|^2 | + | :$${\it \Phi}_x(f_{\rm D}) = {\it \Phi}_n(f_{\rm D}) \cdot | H_{\rm DF}(f_{\rm D}|^2 |
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
Line 84: | Line 84: | ||
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. The red curves were determined simulatively over 100000 samples. You can see: | ||
− | [[File:EN_Mob_A_1_5d.png|right|frame|Approximation of the Jakes spectrum and | + | [[File:EN_Mob_A_1_5d.png|right|frame|Approximation of the Jakes spectrum and autocorrelation function]] |
* The Jakes power spectral density (left graph) can only be reproduced very inaccurately due to the vertical drop at ± f_{\rm D, \ max}. | * The Jakes power spectral density (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 theory suggests. | * For the time domain, this means that the ACF decreases much faster than theory suggests. | ||
− | *For small values of $\ | + | *For small values of $\Delta t$, however, the approximation is very good (right graph). |
{{ML-Fuß}} | {{ML-Fuß}} | ||
Revision as of 14:47, 11 August 2020
In a mobile radio system, the Doppler effect is also noticeable in the power density spectrum 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 Hz. Φz(fD) has only portions within the range ±fD, max, where
- Φz(fD)=2⋅σ2π⋅fD,max⋅√1−(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 autocorrelation function (ACF). The ACF is the Φz(fD) by the checkLink:_Buch_1 ⇒ inverse Fourier transform of the PSD.
With the Bessel function of the first kind and zero order (J0) you get
- φz(Δt)=2σ2⋅J0(2π⋅fD,max⋅Δt).
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:
- This task belongs to the topic of Statistical bindings within the Rayleigh process.
- The digital filter is treated in detail in chapter checkLink:_Buch_3 ⇒ Digital Filter of the book „Stochastic Signal Theory”.
Questions
Solutions
- {\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) LDS {\it \Phi}_n(f_{\rm D}).
- The PSD 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:
- The Jakes power spectral density (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 theory suggests.
- For small values of \Delta t, however, the approximation is very good (right graph).