Aufgaben:Exercise 1.2: Lognormal Channel Model: Unterschied zwischen den Versionen
Javier (Diskussion | Beiträge) K (Javier verschob die Seite Exercises:Exercise 1.2:Lognormal Channel Model nach Exercises:Exercise 1.2: Lognormal Channel Model) |
Javier (Diskussion | Beiträge) |
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− | [[Datei:P_ID2122__Mob_A_1_2.png|right|frame| | + | [[Datei: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 $–80 \ \rm dBm$) |
Zeile 24: | Zeile 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}.$$ |
− | [[ | + | [[File:P_ID2187__Mob_A_1_2c_v1.png|right|frame|loss due to lognormal fading]] |
− | + | The graphic illustrates the result. | |
− | * | + | *The probability density $f_{\rm VS}(V_{\rm S})$ of the path loss due to shadowing (Longnormal–Fading) is shown here. |
*Die Wahrscheinlichkeit, dass das System ausfällt, ist rot markiert: | *Die Wahrscheinlichkeit, dass das System ausfällt, ist rot markiert: | ||
<br clear=all> | <br clear=all> | ||
Zeile 93: | Zeile 93: | ||
{\rm Q}\left ( \frac{120-70-20}{10}\right ) | {\rm Q}\left ( \frac{120-70-20}{10}\right ) | ||
= {\rm Q}(3) \approx 0.001 \hspace{0.05cm}.$$ | = {\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}.$$ | ||
+ | |||
{{ML-Fuß}} | {{ML-Fuß}} | ||
Version vom 25. März 2020, 15:59 Uhr
We consider a mobile radio cell in an urban area and a vehicle that is approximately at a fixed distance $d_0$ from the base station. For example, it moves on an arc around the base station.
Thus the total path loss can be described by the following equation: $$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 $\rm LNTww$ [[Applets:Complementary_Gaussian_Error_Functions_(new_applet)|Complementary_Gaussian_Error_Functions]]. =='"`UNIQ--h-0--QINU`"'=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 ==='"`UNIQ--h-1--QINU`"' sample solution=== [[:Vorlage:ML head]] '''(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}.$$
- Only the limit value $–80 \ \rm dBm$ is required.
(2) Lognormal–Fading with $\sigma_{\rm S} = 0 \ \rm dB$ is equivalent to a constant receive reading $P_{\rm E}$.
- Compared to the subtask '(1) this is $m_{\rm S} = 20 \ \ \rm dB$ smaller ⇒ $P_{\rm E} = \ –60 \ \ \rm dBm$.
- But it is still greater than the specified limit value ($–80 \ \rm dBm$).
- It follows: The system is (almost) 100% functional. „Fast” because with a Gaussian random quantity there is always a (small) residual uncertainty.
(3) The receive power is too low (less than $–80 \ \rm dBm$) if the power loss due to the lognormal–term is $40 \ \rm dB$ or more.
- The variable portion $V_{\rm S}$ must therefore not be greater than $20 \ \rm dB$.
- So it follows:
$${\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}.$$