Difference between revisions of "Aufgaben:Exercise 1.3: Rectangular Functions for Transmitter and Receiver"
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− | {{quiz-Header|Buchseite= | + | {{quiz-Header|Buchseite=Digital_Signal_Transmission/Error_Probability_for_Baseband_Transmission |
}} | }} | ||
− | [[File: P_ID1267__Dig_A_1_3.png|right|frame| | + | [[File: P_ID1267__Dig_A_1_3.png|right|frame|Three different system configurations]] |
− | + | We consider here three variants of a binary bipolar AWGN transmission system which differ with respect to the basic transmission pulse gs(t) as well as the impulse response hE(t) of the receiver filter: | |
− | * | + | *For $\text{System A}$, both gs(t) and hE(t) are rectangular, only the pulse heights $(s_{\rm 0}$ and $1/T)$ are different. |
− | * | + | *$\text{System B} differs from \text{System A}$ by having a triangular-shaped basic transmission pulse with gs(t=0)=s0. |
− | * | + | *$\text{System C} has the same rectangular basic transmission pulse as \text{System A}$, while the impulse response is triangular with hE(t=0)=1/T. |
− | + | The absolute width of the rectangular and triangular functions considered here is $T = 10 \ \rm µ s$ each. The bit rate is R=100 kbit/s. The other system parameters are given as follows: | |
:s0=6√W,N0=2⋅10−5W/Hz. | :s0=6√W,N0=2⋅10−5W/Hz. | ||
− | + | ||
− | * | + | |
− | * | + | |
− | + | Notes: | |
− | * | + | *The exercise belongs to the chapter [[Digital_Signal_Transmission/Error_Probability_for_Baseband_Transmission|"Error Probability for Baseband Transmission"]]. |
+ | *You can use the interactive applet [[Applets:Complementary_Gaussian_Error_Functions|"Complementary Gaussian Error Functions"]] to determine error probabilities. | ||
+ | *Consider [[Theory_of_Stochastic_Signals/Power-Spectral_Density#Wiener-Khintchine_Theorem|"Wiener-Khintchine's theorem"]] when calculating the detection noise power: | ||
:$$ \sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ | :$$ \sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ | ||
+ \infty } {\left| {H_{\rm E}( f )} \right|^2 | + \infty } {\left| {H_{\rm E}( f )} \right|^2 | ||
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− | === | + | ===Questions=== |
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {Calculate for $\text{System A}$ the basic detection pulse gd(t)=gs(t)⋆hE(t). What value g0=gd(t=0) results at time t=0? |
|type="{}"} | |type="{}"} | ||
g0= { 6 3% } W1/2 | g0= { 6 3% } W1/2 | ||
− | { | + | {From this, calculate the detection noise power (variance) σ_{d}^2. |
|type="{}"} | |type="{}"} | ||
σ_{d}^{\hspace{0.02cm}2} \hspace{0.2cm} = \ { 1 3% } \ \rm W | σ_{d}^{\hspace{0.02cm}2} \hspace{0.2cm} = \ { 1 3% } \ \rm W | ||
− | { | + | {Thus, what is the bit error probability p_{\rm B} for $\text{System A}$? |
|type="{}"} | |type="{}"} | ||
p_{\rm B} \hspace{0.2cm} = \ { 0.987 10% } \ \cdot 10^{-9} | p_{\rm B} \hspace{0.2cm} = \ { 0.987 10% } \ \cdot 10^{-9} | ||
− | { | + | {Determine the corresponding quantities for $\text{System B}$ . |
|type="{}"} | |type="{}"} | ||
g_0 \hspace{0.28cm} = \ { 3 3% } \ \rm W^{1/2} | g_0 \hspace{0.28cm} = \ { 3 3% } \ \rm W^{1/2} | ||
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p_{\rm B} \hspace{0.2cm} = \ { 0.135 10% } \ \cdot 10^{-2} | p_{\rm B} \hspace{0.2cm} = \ { 0.135 10% } \ \cdot 10^{-2} | ||
− | { | + | {What are the characteristics for $\text{System C}$ ? |
|type="{}"} | |type="{}"} | ||
g_0 \hspace{0.28cm} = \ { 3 3% } \ \rm W^{1/2} | g_0 \hspace{0.28cm} = \ { 3 3% } \ \rm W^{1/2} | ||
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</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''1.''' | + | '''1.''' For '''System A''', the convolution of the two equal-width rectangular functions g_{s}(t) and h_{\rm E}(t) leads to a triangular detection pulse with the maximum at t = 0: |
:$$g_d (t = 0) = \int_{ - T/2}^{ | :$$g_d (t = 0) = \int_{ - T/2}^{ | ||
+ T/2} { g_s(t) \cdot h_{\rm E}( t )} \hspace{0.1cm}{\rm{d}}t =s_0 | + T/2} { g_s(t) \cdot h_{\rm E}( t )} \hspace{0.1cm}{\rm{d}}t =s_0 | ||
\cdot \frac{1 }{T} \cdot T = s_0 \hspace{0.1cm}\underline { = 6 \,\,\sqrt{{\rm | \cdot \frac{1 }{T} \cdot T = s_0 \hspace{0.1cm}\underline { = 6 \,\,\sqrt{{\rm | ||
W}}}\hspace{0.05cm}.$$ | W}}}\hspace{0.05cm}.$$ | ||
− | + | There is no intersymbol interfering because for | t |\ge T the detection pulse is g_{d}(t) = 0. | |
− | '''2.''' | + | '''2.''' The variance of the noise component of the detection signal – referred to as the "detection noise power" – can be calculated in both the time and frequency domains. |
+ | *For the present rectangular waveform, calculation in the time domain yields faster results: | ||
:$$\sigma _d ^2 \ = \ \frac{N_0 }{2} \cdot \int_{ - | :$$\sigma _d ^2 \ = \ \frac{N_0 }{2} \cdot \int_{ - | ||
\infty }^{ + \infty } {\left| {h_{\rm E}( t )} \right|^2 | \infty }^{ + \infty } {\left| {h_{\rm E}( t )} \right|^2 | ||
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\,\,{\rm W/Hz}}{2 \cdot 10^{-5} \,\,{\rm s}} \hspace{0.1cm}\underline {= 1\,{\rm | \,\,{\rm W/Hz}}{2 \cdot 10^{-5} \,\,{\rm s}} \hspace{0.1cm}\underline {= 1\,{\rm | ||
W}}\hspace{0.05cm}.$$ | W}}\hspace{0.05cm}.$$ | ||
− | + | *The frequency domain calculation would be as follows with $H_{\rm E}(f) = {\rm sinc}(fT)$: | |
:$$\sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ | :$$\sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ | ||
+ \infty } {\left| {H_{\rm E}( f )} \right|^2 | + \infty } {\left| {H_{\rm E}( f )} \right|^2 | ||
\hspace{0.1cm}{\rm{d}}f} = \frac{N_0 }{2} \cdot \int_{- | \hspace{0.1cm}{\rm{d}}f} = \frac{N_0 }{2} \cdot \int_{- | ||
− | \infty }^{ \infty } {\rm | + | \infty }^{ \infty } {\rm sinc}^2(f T)\hspace{0.1cm}{\rm{d}}f = |
\frac{N_0 }{2T} \hspace{0.05cm}.$$ | \frac{N_0 }{2T} \hspace{0.05cm}.$$ | ||
− | '''3.''' | + | |
+ | '''3.''' Due to the time-limited pulse shape (this means: no intersymbol interfering!), the bipolar approach assumed here yields: | ||
:$$p_{\rm B} = {\rm Q} \left( \frac{s_0}{\sigma_d}\right)= {\rm Q} \left( \frac{ 6 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) | :$$p_{\rm B} = {\rm Q} \left( \frac{s_0}{\sigma_d}\right)= {\rm Q} \left( \frac{ 6 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) | ||
= {\rm Q}(6) \hspace{0.1cm}\underline {= 0.987 \cdot 10^{-9}} \hspace{0.05cm}.$$ | = {\rm Q}(6) \hspace{0.1cm}\underline {= 0.987 \cdot 10^{-9}} \hspace{0.05cm}.$$ | ||
− | System A | + | *'''System A''' represents the matched filter realization of the optimal binary receiver, so the following equations would also be applicable: |
:$$E_{\rm B} = s_0^2 \cdot T = 36\, {\rm W} \cdot 10^{-5} {\rm s}\hspace{0.3cm} | :$$E_{\rm B} = s_0^2 \cdot T = 36\, {\rm W} \cdot 10^{-5} {\rm s}\hspace{0.3cm} | ||
\Rightarrow \hspace{0.3cm} p_{\rm B} = {\rm Q} \left( \sqrt{\frac{2 \cdot E_{\rm B}}{N_0}}\right) | \Rightarrow \hspace{0.3cm} p_{\rm B} = {\rm Q} \left( \sqrt{\frac{2 \cdot E_{\rm B}}{N_0}}\right) | ||
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\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | '''4.''' | + | |
+ | '''4.''' Since '''System B''' uses the same receiver filter as '''System A''', the same detection noise power σ_{d}^2 = 1 \ \rm W is also obtained. | ||
+ | *However, the basic detection pulse is now no longer triangular, but has a more pointed shape. At time t = 0 applies: | ||
:$$g_d (t = 0) = \frac{1}{T} \cdot \int_{ - T/2}^{ | :$$g_d (t = 0) = \frac{1}{T} \cdot \int_{ - T/2}^{ | ||
+ T/2} { g_s(t) } \hspace{0.1cm}{\rm{d}}t = \frac{1}{T} \cdot | + T/2} { g_s(t) } \hspace{0.1cm}{\rm{d}}t = \frac{1}{T} \cdot | ||
\frac{s_0 }{2} \cdot T = \frac{s_0 }{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm | \frac{s_0 }{2} \cdot T = \frac{s_0 }{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm | ||
W}}\hspace{0.05cm}.$$ | W}}\hspace{0.05cm}.$$ | ||
− | + | *'''System B''' is also free of intersymbol interfering. Therefore, one obtains for the bit error probability: | |
:$$p_{\rm B} = {\rm Q} \left( \frac{g_d (t = 0)}{\sigma_d}\right)= {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) | :$$p_{\rm B} = {\rm Q} \left( \frac{g_d (t = 0)}{\sigma_d}\right)= {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) | ||
= {\rm Q}(3) \hspace{0.1cm}\underline {= 0.135 \cdot 10^{-2}} \hspace{0.05cm}.$$ | = {\rm Q}(3) \hspace{0.1cm}\underline {= 0.135 \cdot 10^{-2}} \hspace{0.05cm}.$$ | ||
− | + | *On the other hand, the following calculation is '''not''' applicable here: | |
:$$E_{\rm B} = \int^{+\infty} _{-\infty} g_s^2(t)\,{\rm | :$$E_{\rm B} = \int^{+\infty} _{-\infty} g_s^2(t)\,{\rm | ||
d}t = 2\cdot s_0^2 \cdot \int ^{+T/2} _{0} \left( 1- \frac{2t}{T}\right)^2\,{\rm | d}t = 2\cdot s_0^2 \cdot \int ^{+T/2} _{0} \left( 1- \frac{2t}{T}\right)^2\,{\rm | ||
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={\rm Q} \left( \sqrt{12}\right)={\rm Q}(3.464) \approx 3 \cdot 10^{-4} | ={\rm Q} \left( \sqrt{12}\right)={\rm Q}(3.464) \approx 3 \cdot 10^{-4} | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | + | *One would thus compute a bit error probability that is too low, since the implicit assumption of a matched filter does not hold. | |
+ | |||
− | '''5.''' | + | '''5.''' For the rectangular basic transmission pulse and the triangular impulse response ⇒ '''System C''', <br>the same basic detection pulse is obtained as for the triangular g_{\rm s}(t) and the rectangular h_{\rm E}(t). |
+ | *Therefore, as in '''System B''': | ||
:$$g_d (t = 0) = \frac{s_0}{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm | :$$g_d (t = 0) = \frac{s_0}{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm | ||
W}}\hspace{0.05cm}.$$ | W}}\hspace{0.05cm}.$$ | ||
− | + | *In contrast, the detection noise power is now smaller than in systems '''A''' and '''B''': | |
:$$\sigma _d ^2 = \frac{N_0}{2} \cdot \frac{1}{T^2} \cdot \int^{+T/2} _{-T/2} \left( 1- \frac{2t}{T}\right)^2\,{\rm | :$$\sigma _d ^2 = \frac{N_0}{2} \cdot \frac{1}{T^2} \cdot \int^{+T/2} _{-T/2} \left( 1- \frac{2t}{T}\right)^2\,{\rm | ||
d}t = \frac{N_0}{6T}\hspace{0.1cm}\underline { = 0.333 \,{\rm W}}.$$ | d}t = \frac{N_0}{6T}\hspace{0.1cm}\underline { = 0.333 \,{\rm W}}.$$ | ||
− | + | *This now gives us for the bit error probability: | |
:$$p_{\rm B} = {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{0.577 \,\sqrt{\rm W}}\right) | :$$p_{\rm B} = {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{0.577 \,\sqrt{\rm W}}\right) | ||
\approx {\rm Q}(5.2)\hspace{0.1cm}\underline { \approx 10^{-7} } \hspace{0.05cm}.$$ | \approx {\rm Q}(5.2)\hspace{0.1cm}\underline { \approx 10^{-7} } \hspace{0.05cm}.$$ | ||
− | + | *The apparent increase in error probability by a factor of about 100 compared to subtask '''(3)''' is due to the severe mismatch compared to the matched filter. | |
+ | *The improvement over subtask '''(4)''' is due to the higher signal energy. | ||
{{ML-Fuß}} | {{ML-Fuß}} | ||
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− | [[Category: | + | [[Category:Digital Signal Transmission: Exercises|^1.2 BER for Baseband Systems^]] |
Latest revision as of 09:07, 20 May 2022
We consider here three variants of a binary bipolar AWGN transmission system which differ with respect to the basic transmission pulse g_{s}(t) as well as the impulse response h_{\rm E}(t) of the receiver filter:
- For \text{System A}, both g_{s}(t) and h_{\rm E}(t) are rectangular, only the pulse heights (s_{\rm 0} and 1/T) are different.
- \text{System B} differs from \text{System A} by having a triangular-shaped basic transmission pulse with g_{s}(t=0) = s_{\rm 0}.
- \text{System C} has the same rectangular basic transmission pulse as \text{System A}, while the impulse response is triangular with h_{\rm E}(t=0) = 1/T.
The absolute width of the rectangular and triangular functions considered here is T = 10 \ \rm µ s each. The bit rate is R = 100 \ \rm kbit/s. The other system parameters are given as follows:
- s_0 = 6 \,\,\sqrt{W}\hspace{0.05cm},\hspace{0.3cm} N_{\rm 0} = 2 \cdot 10^{-5} \,\,{\rm W/Hz}\hspace{0.05cm}.
Notes:
- The exercise belongs to the chapter "Error Probability for Baseband Transmission".
- You can use the interactive applet "Complementary Gaussian Error Functions" to determine error probabilities.
- Consider "Wiener-Khintchine's theorem" when calculating the detection noise power:
- \sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ + \infty } {\left| {H_{\rm E}( f )} \right|^2 \hspace{0.1cm}{\rm{d}}f} = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ + \infty } {\left| {h_{\rm E}( t )} \right|^2 \hspace{0.1cm}{\rm{d}}t}\hspace{0.05cm}.
Questions
Solution
- g_d (t = 0) = \int_{ - T/2}^{ + T/2} { g_s(t) \cdot h_{\rm E}( t )} \hspace{0.1cm}{\rm{d}}t =s_0 \cdot \frac{1 }{T} \cdot T = s_0 \hspace{0.1cm}\underline { = 6 \,\,\sqrt{{\rm W}}}\hspace{0.05cm}.
There is no intersymbol interfering because for | t |\ge T the detection pulse is g_{d}(t) = 0.
2. The variance of the noise component of the detection signal – referred to as the "detection noise power" – can be calculated in both the time and frequency domains.
- For the present rectangular waveform, calculation in the time domain yields faster results:
- \sigma _d ^2 \ = \ \frac{N_0 }{2} \cdot \int_{ - \infty }^{ + \infty } {\left| {h_{\rm E}( t )} \right|^2 \hspace{0.1cm}{\rm{d}}t} =\frac{N_0 }{2} \cdot \int_{ - T/2 }^{ + T/2 } {\left| {h_{\rm E}( t )} \right|^2 \hspace{0.1cm}{\rm{d}}t} = \ \frac{N_0 }{2} \cdot\frac{1 }{T^2} \cdot T = \frac{N_0 }{2T} = \frac{2 \cdot 10^{-5} \,\,{\rm W/Hz}}{2 \cdot 10^{-5} \,\,{\rm s}} \hspace{0.1cm}\underline {= 1\,{\rm W}}\hspace{0.05cm}.
- The frequency domain calculation would be as follows with H_{\rm E}(f) = {\rm sinc}(fT):
- \sigma _d ^2 = \frac{N_0 }{2} \cdot \int_{ - \infty }^{ + \infty } {\left| {H_{\rm E}( f )} \right|^2 \hspace{0.1cm}{\rm{d}}f} = \frac{N_0 }{2} \cdot \int_{- \infty }^{ \infty } {\rm sinc}^2(f T)\hspace{0.1cm}{\rm{d}}f = \frac{N_0 }{2T} \hspace{0.05cm}.
3. Due to the time-limited pulse shape (this means: no intersymbol interfering!), the bipolar approach assumed here yields:
- p_{\rm B} = {\rm Q} \left( \frac{s_0}{\sigma_d}\right)= {\rm Q} \left( \frac{ 6 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) = {\rm Q}(6) \hspace{0.1cm}\underline {= 0.987 \cdot 10^{-9}} \hspace{0.05cm}.
- System A represents the matched filter realization of the optimal binary receiver, so the following equations would also be applicable:
- E_{\rm B} = s_0^2 \cdot T = 36\, {\rm W} \cdot 10^{-5} {\rm s}\hspace{0.3cm} \Rightarrow \hspace{0.3cm} p_{\rm B} = {\rm Q} \left( \sqrt{\frac{2 \cdot E_{\rm B}}{N_0}}\right) ={\rm Q} \left( \sqrt{\frac{2 \cdot 36 \cdot 10^{-5}\,\, {\rm Ws}}{2 \cdot 10^{-5} \,\, {\rm Ws}}}\right)={\rm Q}(6) \hspace{0.05cm}.
4. Since System B uses the same receiver filter as System A, the same detection noise power σ_{d}^2 = 1 \ \rm W is also obtained.
- However, the basic detection pulse is now no longer triangular, but has a more pointed shape. At time t = 0 applies:
- g_d (t = 0) = \frac{1}{T} \cdot \int_{ - T/2}^{ + T/2} { g_s(t) } \hspace{0.1cm}{\rm{d}}t = \frac{1}{T} \cdot \frac{s_0 }{2} \cdot T = \frac{s_0 }{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm W}}\hspace{0.05cm}.
- System B is also free of intersymbol interfering. Therefore, one obtains for the bit error probability:
- p_{\rm B} = {\rm Q} \left( \frac{g_d (t = 0)}{\sigma_d}\right)= {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{1 \,\sqrt{\rm W}}\right) = {\rm Q}(3) \hspace{0.1cm}\underline {= 0.135 \cdot 10^{-2}} \hspace{0.05cm}.
- On the other hand, the following calculation is not applicable here:
- E_{\rm B} = \int^{+\infty} _{-\infty} g_s^2(t)\,{\rm d}t = 2\cdot s_0^2 \cdot \int ^{+T/2} _{0} \left( 1- \frac{2t}{T}\right)^2\,{\rm d}t = \frac{s_0^2 \cdot T }{3} = 12 \cdot 10^{-5} \,{\rm Ws}
- \Rightarrow \hspace{0.3cm} p_{\rm B} = {\rm Q} \left( \sqrt{\frac{2 \cdot E_{\rm B}}{N_0}}\right) ={\rm Q} \left( \sqrt{12}\right)={\rm Q}(3.464) \approx 3 \cdot 10^{-4} \hspace{0.05cm}.
- One would thus compute a bit error probability that is too low, since the implicit assumption of a matched filter does not hold.
5. For the rectangular basic transmission pulse and the triangular impulse response ⇒ System C,
the same basic detection pulse is obtained as for the triangular g_{\rm s}(t) and the rectangular h_{\rm E}(t).
- Therefore, as in System B:
- g_d (t = 0) = \frac{s_0}{2}\hspace{0.1cm}\underline {= 3 \,\,\sqrt{\rm W}}\hspace{0.05cm}.
- In contrast, the detection noise power is now smaller than in systems A and B:
- \sigma _d ^2 = \frac{N_0}{2} \cdot \frac{1}{T^2} \cdot \int^{+T/2} _{-T/2} \left( 1- \frac{2t}{T}\right)^2\,{\rm d}t = \frac{N_0}{6T}\hspace{0.1cm}\underline { = 0.333 \,{\rm W}}.
- This now gives us for the bit error probability:
- p_{\rm B} = {\rm Q} \left( \frac{ 3 \,\sqrt{\rm W}}{0.577 \,\sqrt{\rm W}}\right) \approx {\rm Q}(5.2)\hspace{0.1cm}\underline { \approx 10^{-7} } \hspace{0.05cm}.
- The apparent increase in error probability by a factor of about 100 compared to subtask (3) is due to the severe mismatch compared to the matched filter.
- The improvement over subtask (4) is due to the higher signal energy.