Aufgaben:Exercise 1.2: Lognormal Channel Model: Difference between revisions
m Javier verschob die Seite Exercises:Exercise 1.2:Lognormal Channel Model nach Exercises:Exercise 1.2: Lognormal Channel Model |
No edit summary |
||
| Line 2: | Line 2: | ||
}} | }} | ||
[[File:P_ID2122__Mob_A_1_2.png|right|frame| | [[File:P_ID2122__Mob_A_1_2.png|right|frame|PDF of lognormal fading]] | ||
We consider a mobile radio cell in an urban area and a vehicle that is approximately at a fixed distance $d_0$ from the base station. For example, it moves on an arc around the base station. | |||
Thus the total path loss can be described by the following equation: | |||
$$V_{\rm P} = V_{\rm 0} + V_{\rm S} \hspace{0.05cm}.$$ | |||
*$V_0$ | *$V_0$ takes into account the distance-dependent path loss which is assumed to be constant with $V_0 = 80 \ \rm dB$ . | ||
* | *The loss $V_{\rm S}$ is due to shadowing (<i>Shadowing</i>) caused by the lognormal–distribution with the probability density function | ||
$$f_{V{\rm S}}(V_{\rm S}) = \frac {1}{ \sqrt{2 \pi }\cdot \sigma_{\rm S} \cdot {\rm exp } \left [ - \frac{ (V_{\rm S}- m_{\rm S})^2}{2 \cdot \sigma_{\rm S}^2} \right ] \hspace{0.05cm}$$ | |||
: | :is described in sufficient detail (see diagram). The following numerical values apply: | ||
$$m_{\rm S} = 20\,\,{\rm dB}\hspace{0.05cm},\hspace{0.2cm} \sigma_{\rm S} = 10\,\,{\rm dB}\hspace{0.15cm}{\rm or }\hspace{0.15cm}\sigma_{\rm S} = 0\,\,{\rm dB}\hspace{0.15cm}{\rm (subtask\hspace{0.15cm} 2)}\hspace{0.05cm}.$$ | |||
Also make the following simple assumptions: | |||
* | * The transmit power is $P_{\rm S} = 10 \ \rm W$ (or $40 \ \rm dBm$). | ||
* | * The receive power should be at least $P_{\rm E} = 10 \ \rm pW$ (or $–80 \ \rm dBm$) | ||
| Line 24: | Line 24: | ||
Notes:'' | |||
* | * The task belongs to the chapter [[Mobile_Communication/Distancedependent%C3%A4ngige_D%C3%A4mpfung_und_Abschattung|Distance-dependent attenuation and shading]]. | ||
* | * You can use the following (rough) approximations for the complementary Gaussian error integral: | ||
$${\rm Q}(1) \approx 0.16\hspace{0.05cm},\hspace{0.2cm} {\rm Q}(2) \approx 0.02\hspace{0.05cm},\hspace{0.2cm} | |||
{\rm Q}(3) \approx 10^{-3}\hspace{0.05cm}. | {\rm Q}(3) \approx 10^{-3}\hspace{0.05cm}.$ | ||
* | * Or use the interaction module provided by $\rm LNTww$ [[Applets:Complementary_Gaussian_Error_Functions_(new_applet)|Complementary_Gaussian_Error_Functions]]. | ||
=== | ===Questionnaire== | ||
<quiz display=simple> | <quiz display=simple> | ||
{ | {Would $P_{\rm E}$ without consideration of the lognormal–fading be sufficient? | ||
|type="()"} | |type="()"} | ||
+ | + Yes, | ||
- | - No. | ||
The log normal andash parameters are $m_{\rm S} = 20 \, \rm dB$ and $\sigma_{\rm S} = 0 \, \rm dB$. What percentage of the time does the system work? | |||
|type="{}"} | |type="{}"} | ||
${\rm Pr(System \ | ${\rm Pr(System \ works)} \ = \ $ { 100 3% } $\ \%$ | ||
{ | {What is the probability with $m_{\rm S} = 20 \ \ \rm dB$ and $\sigma_{\rm S} = 10 \ \ \rm dB$? | ||
|type="{}"} | |type="{}"} | ||
${\rm Pr(System \ | ${\rm Pr(System \ works)}\ = \ $ { 98 3% } $\ \%$ | ||
{ | {How big can $V_0$ be maximum, so that the reliability to $99.9\%$ is reached? | ||
|type="{}"} | |type="{}"} | ||
$V_0 \ = \ $ { 70 3% } $\ \rm dB$ | $V_0 \ = \ $ { 70 3% } $\ \ \rm dB$ | ||
</quiz | </quiz | ||
=== | === sample solution=== | ||
{{ML | {{ML head}} | ||
'''(1)''' | '''(1)''' Correct is <u>YES</u>: | ||
* | *From the $\rm dB$–value $V_0 = 80 \ \rm dB$ follows the absolute (linear) value $K_0 = 10^8$. Thus the received power is | ||
$$P_{\rm E} = P_{\rm S}/K_0 = 10 \ {\rm W}/10^8 = 100 \ {\rm nW} > 10 \ \ \rm pW.$$ | |||
* | *You can also solve this problem directly with the logarithmic quantities: | ||
$10 \cdot {\rm lg}\hspace{0.15cm} \frac{P_{\rm E}}}{1\,\,{\rm mW}} = 10 \cdot {\rm lg}\hspace{0.15cm} \frac{P_{\rm S}}{1\,\,{\rm mW}} - V_0 = 40\,{\rm dBm} -80\,\,{\rm dB} = -40\,\,{\rm dBm} | |||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
* | *Only the limit value $–80 \ \rm dBm$ is required. | ||
'''(2)''' Lognormal–Fading | '''(2)''' Lognormal–Fading with $\sigma_{\rm S} = 0 \ \rm dB$ is equivalent to a constant receive reading $P_{\rm E}$. | ||
* | *Compared to the subtask '''(1)'' this is $m_{\rm S} = 20 \ \ \rm dB$ smaller ⇒ $P_{\rm E} = \ –60 \ \ \rm dBm$. | ||
* | *But it is still greater than the specified limit value ($–80 \ \rm dBm$). | ||
* | *It follows: The system is (almost) <u>100% functional</u>. „Fast” because with a Gaussian random quantity there is always a (small) residual uncertainty. | ||
'''(3)''' | '''(3)''' The receive power is too low (less than $–80 \ \rm dBm$) if the power loss due to the lognormal–term is $40 \ \rm dB$ or more. | ||
* | *The variable portion $V_{\rm S}$ must therefore not be greater than $20 \ \rm dB$. | ||
* | *So it follows: | ||
$${\rm Pr}({\rm "System\hspace{0.15cm} does not work\hspace{0.15cm}"})= {\rm Q}\left ( \frac{20\,\,{\rm dB}}}{\sigma_{\rm S} = 10\,{\rm dB}\right ) | |||
= {\rm Q}(2) \approx 0.02\hspace{0.3cm} | = {\rm Q}(2) \approx 0.02\hspace{0.3cm} | ||
\Rightarrow \hspace{0.3cm}{\rm Pr}({\rm "System\hspace{0.15cm} | \Rightarrow \hspace{0.3cm}{\rm Pr}({\rm "System\hspace{0.15cm} works"})= 1- 0.02 \hspace{0.15cm} \underline{\approx 98\,\%}\hspace{0.05cm}.$$ | ||
[[File:P_ID2187__Mob_A_1_2c_v1.png|right|frame| | [[File:P_ID2187__Mob_A_1_2c_v1.png|right|frame|loss due to lognormal fading]] | ||
The graphic illustrates the result. | |||
* | *The probability density $f_{\rm VS}(V_{\rm S})$ of the path loss due to shadowing (Longnormal–Fading) is shown here. | ||
*Die Wahrscheinlichkeit, dass das System ausfällt, ist rot markiert: | *Die Wahrscheinlichkeit, dass das System ausfällt, ist rot markiert: | ||
<br clear=all> | <br clear=all> | ||
| Line 93: | Line 93: | ||
{\rm Q}\left ( \frac{120-70-20}{10}\right ) | {\rm Q}\left ( \frac{120-70-20}{10}\right ) | ||
= {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$ | = {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$ | ||
*The probability that the system will fail is marked in red: | |||
<br clear=all> | |||
'''(4)''' From the availability probability $99.9 \%$ follows the default probability $10^{\rm –3} \approx \ {\rm Q}(3)$. | |||
*If the distance-dependent path loss $V_0$ is reduced by $10 \ \ \rm dB$ to $\underline {70 \ \rm dB}$, a failure will only occur when $V_{\rm S} ≥ 50 \ \ \rm dB$. | |||
*This would achieve exactly the required reliability, as the following calculation shows: | |||
$${\rm Pr}({\rm "System\hspace{0.15cm} does not work\hspace{0.15cm}"})= | |||
{\rm Q}\left ( \frac{120-70-20}{10}\right ) | |||
= {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$ | |||
{{ML-Fuß}} | {{ML-Fuß}} | ||
Revision as of 16:59, 25 March 2020

We consider a mobile radio cell in an urban area and a vehicle that is approximately at a fixed distance $d_0$ from the base station. For example, it moves on an arc around the base station.
Thus the total path loss can be described by the following equation: $$V_{\rm P} = V_{\rm 0} + V_{\rm S} \hspace{0.05cm}.$$
- $V_0$ takes into account the distance-dependent path loss which is assumed to be constant with $V_0 = 80 \ \rm dB$ .
- The loss $V_{\rm S}$ is due to shadowing (Shadowing) caused by the lognormal–distribution with the probability density function
$$f_{V{\rm S}}(V_{\rm S}) = \frac {1}{ \sqrt{2 \pi }\cdot \sigma_{\rm S} \cdot {\rm exp } \left [ - \frac{ (V_{\rm S}- m_{\rm S})^2}{2 \cdot \sigma_{\rm S}^2} \right ] \hspace{0.05cm}$$
- is described in sufficient detail (see diagram). The following numerical values apply:
$$m_{\rm S} = 20\,\,{\rm dB}\hspace{0.05cm},\hspace{0.2cm} \sigma_{\rm S} = 10\,\,{\rm dB}\hspace{0.15cm}{\rm or }\hspace{0.15cm}\sigma_{\rm S} = 0\,\,{\rm dB}\hspace{0.15cm}{\rm (subtask\hspace{0.15cm} 2)}\hspace{0.05cm}.$$
Also make the following simple assumptions:
- The transmit power is $P_{\rm S} = 10 \ \rm W$ (or $40 \ \rm dBm$).
- The receive power should be at least $P_{\rm E} = 10 \ \rm pW$ (or $–80 \ \rm dBm$)
Notes:
- The task belongs to the chapter Distance-dependent attenuation and shading.
- You can use the following (rough) approximations for the complementary Gaussian error integral:
$${\rm Q}(1) \approx 0.16\hspace{0.05cm},\hspace{0.2cm} {\rm Q}(2) \approx 0.02\hspace{0.05cm},\hspace{0.2cm}
{\rm Q}(3) \approx 10^{-3}\hspace{0.05cm}.$
- Or use the interaction module provided by $\rm LNTww$ Complementary_Gaussian_Error_Functions.
=Questionnaire
<quiz display=simple> {Would $P_{\rm E}$ without consideration of the lognormal–fading be sufficient? |type="()"} + Yes, - No.
The log normal andash parameters are $m_{\rm S} = 20 \, \rm dB$ and $\sigma_{\rm S} = 0 \, \rm dB$. What percentage of the time does the system work? |type="{}"} ${\rm Pr(System \ works)} \ = \ $ { 100 3% } $\ \%$
{What is the probability with $m_{\rm S} = 20 \ \ \rm dB$ and $\sigma_{\rm S} = 10 \ \ \rm dB$? |type="{}"} ${\rm Pr(System \ works)}\ = \ $ { 98 3% } $\ \%$
{How big can $V_0$ be maximum, so that the reliability to $99.9\%$ is reached? |type="{}"} $V_0 \ = \ $ { 70 3% } $\ \ \rm dB$ </quiz
sample solution
Template:ML head (1) Correct is YES:
- From the $\rm dB$–value $V_0 = 80 \ \rm dB$ follows the absolute (linear) value $K_0 = 10^8$. Thus the received power is
$$P_{\rm E} = P_{\rm S}/K_0 = 10 \ {\rm W}/10^8 = 100 \ {\rm nW} > 10 \ \ \rm pW.$$
- You can also solve this problem directly with the logarithmic quantities:
$10 \cdot {\rm lg}\hspace{0.15cm} \frac{P_{\rm E}}}{1\,\,{\rm mW}} = 10 \cdot {\rm lg}\hspace{0.15cm} \frac{P_{\rm S}}{1\,\,{\rm mW}} - V_0 = 40\,{\rm dBm} -80\,\,{\rm dB} = -40\,\,{\rm dBm}
\hspace{0.05cm}.$$
- Only the limit value $–80 \ \rm dBm$ is required.
(2) Lognormal–Fading with $\sigma_{\rm S} = 0 \ \rm dB$ is equivalent to a constant receive reading $P_{\rm E}$.
- Compared to the subtask '(1) this is $m_{\rm S} = 20 \ \ \rm dB$ smaller ⇒ $P_{\rm E} = \ –60 \ \ \rm dBm$.
- But it is still greater than the specified limit value ($–80 \ \rm dBm$).
- It follows: The system is (almost) 100% functional. „Fast” because with a Gaussian random quantity there is always a (small) residual uncertainty.
(3) The receive power is too low (less than $–80 \ \rm dBm$) if the power loss due to the lognormal–term is $40 \ \rm dB$ or more.
- The variable portion $V_{\rm S}$ must therefore not be greater than $20 \ \rm dB$.
- So it follows:
$${\rm Pr}({\rm "System\hspace{0.15cm} does not work\hspace{0.15cm}"})= {\rm Q}\left ( \frac{20\,\,{\rm dB}}}{\sigma_{\rm S} = 10\,{\rm dB}\right )
= {\rm Q}(2) \approx 0.02\hspace{0.3cm}
\Rightarrow \hspace{0.3cm}{\rm Pr}({\rm "System\hspace{0.15cm} works"})= 1- 0.02 \hspace{0.15cm} \underline{\approx 98\,\%}\hspace{0.05cm}.$$

The graphic illustrates the result.
- The probability density $f_{\rm VS}(V_{\rm S})$ of the path loss due to shadowing (Longnormal–Fading) is shown here.
- Die Wahrscheinlichkeit, dass das System ausfällt, ist rot markiert:
(4) Aus der Verfügbarkeitswahrscheinlichkeit $99.9 \%$ folgt die Ausfallwahrscheinlichkeit $10^{\rm –3} \approx \ {\rm Q}(3)$.
- Verringert man den entfernungsabhängigen Pfadverlust $V_0$ um $10 \ \rm dB$ auf $\underline {70 \ \rm dB}$, so kommt es erst dann zu einem Ausfall, wenn $V_{\rm S} ≥ 50 \ \rm dB$ ist.
- Damit wäre genau die geforderte Zuverlässigkeit erreicht, wie die folgende Rechnung zeigt:
- $${\rm Pr}({\rm "System\hspace{0.15cm}funktioniert\hspace{0.15cm}nicht"})=
{\rm Q}\left ( \frac{120-70-20}{10}\right )
= {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$
- The probability that the system will fail is marked in red:
(4) From the availability probability $99.9 \%$ follows the default probability $10^{\rm –3} \approx \ {\rm Q}(3)$.
- If the distance-dependent path loss $V_0$ is reduced by $10 \ \ \rm dB$ to $\underline {70 \ \rm dB}$, a failure will only occur when $V_{\rm S} ≥ 50 \ \ \rm dB$.
- This would achieve exactly the required reliability, as the following calculation shows:
$${\rm Pr}({\rm "System\hspace{0.15cm} does not work\hspace{0.15cm}"})=
{\rm Q}\left ( \frac{120-70-20}{10}\right )
= {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$