Difference between revisions of "Aufgaben:Exercise 5.5: Multi-User Interference"

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m (Text replacement - "rms value" to "standard deviation")
 
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* The bit error probability without interfering subscribers in the same frequency band is:
 
* The bit error probability without interfering subscribers in the same frequency band is:
 
:$$p_{\rm B} = {\rm Q} \left ( \sqrt{ {2\cdot E_{\rm B}}/{N_{\rm 0}}}\right ) \approx {\rm Q} \left ( \sqrt{2 \cdot 3.162}\right ) = {\rm Q} \left ( 2.515 \right ) \approx 6 \cdot 10^{-3} \hspace{0.05cm}.$$
 
:$$p_{\rm B} = {\rm Q} \left ( \sqrt{ {2\cdot E_{\rm B}}/{N_{\rm 0}}}\right ) \approx {\rm Q} \left ( \sqrt{2 \cdot 3.162}\right ) = {\rm Q} \left ( 2.515 \right ) \approx 6 \cdot 10^{-3} \hspace{0.05cm}.$$
* Since in the absence of interfering subscribers all payload samples are equal to  $±s_0$   ("Nyquist system"),  the bit error probability with the noise effective value  $σ_d$  before the decision maker  $($originating from the AWGN noise$)$  is given as follows:    
+
* The value of the signal at sampling time, without noise or interfering subscribers, is equal to $±s_0$   ("Nyquist system").  As a result, the bit error probability for noise standard deviation  $σ_d$  before the decision stage $($ originating from the AWGN noise$)$  can be stated as follows:    
 
:$$p_{\rm B} = {\rm Q} \left ( {s_0}/{\sigma_d}\right ) \hspace{0.05cm}.$$
 
:$$p_{\rm B} = {\rm Q} \left ( {s_0}/{\sigma_d}\right ) \hspace{0.05cm}.$$
  
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*The exercise belongs to the chapter  [[Modulation_Methods/Error_Probability_of_Direct-Sequence_Spread_Spectrum_Modulation|Error Probability of Direct-Sequence Spread Spectrum Modulation]].
 
*The exercise belongs to the chapter  [[Modulation_Methods/Error_Probability_of_Direct-Sequence_Spread_Spectrum_Modulation|Error Probability of Direct-Sequence Spread Spectrum Modulation]].
 
*Reference is made in particular to the section  [[Modulation_Methods/Error_Probability_of_Direct-Sequence_Spread_Spectrum_Modulation#Two_users_with_M-sequence_spreading|Two users with M-sequence spreading]].   
 
*Reference is made in particular to the section  [[Modulation_Methods/Error_Probability_of_Direct-Sequence_Spread_Spectrum_Modulation#Two_users_with_M-sequence_spreading|Two users with M-sequence spreading]].   
*For the so-called [[Applets:Complementary_Gaussian_Error_Functions|Q-function]],  the following approximations can be assumed:
+
*For the so-called  [[Applets:Complementary_Gaussian_Error_Functions|Q-function]],  the following approximations can be assumed:
 
:$$ {\rm Q} (2) \approx 0.02275, \hspace{0.2cm}{\rm Q} (3) \approx 0.00135, \hspace{0.2cm}{\rm Q} (5) \approx 2.45 \cdot 10^{-7} \hspace{0.05cm}.$$
 
:$$ {\rm Q} (2) \approx 0.02275, \hspace{0.2cm}{\rm Q} (3) \approx 0.00135, \hspace{0.2cm}{\rm Q} (5) \approx 2.45 \cdot 10^{-7} \hspace{0.05cm}.$$
  
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<quiz display=simple>
 
<quiz display=simple>
{What is the&nbsp; (normalized)&nbsp; noise rms value at the decision maker?
+
{What is the&nbsp; (normalized)&nbsp; noise standard deviation at the decision maker?
 
|type="{}"}
 
|type="{}"}
 
$σ_d/s_0 \ = \ $  { 0.4 3% }  
 
$σ_d/s_0 \ = \ $  { 0.4 3% }  
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===Solution===
 
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp;  From the two equations given above, it follows directly:
+
'''(1)'''&nbsp;  From the two equations given above,&nbsp; it follows directly:
 
:$$p_{\rm B} = {\rm Q}(2.515) = {\rm Q}({s_0}/{\sigma_d}) \hspace{0.3cm}\Rightarrow\hspace{0.3cm} \frac{\sigma_d}{s_0} = \frac{1}{2.515} = 0.398 \hspace{0.15cm}\underline {\approx 0.4} \hspace{0.05cm}.$$
 
:$$p_{\rm B} = {\rm Q}(2.515) = {\rm Q}({s_0}/{\sigma_d}) \hspace{0.3cm}\Rightarrow\hspace{0.3cm} \frac{\sigma_d}{s_0} = \frac{1}{2.515} = 0.398 \hspace{0.15cm}\underline {\approx 0.4} \hspace{0.05cm}.$$
*However, one could also calculate this quantity using the more general equation
+
*However,&nbsp; one could also calculate this quantity using the more general equation
:$$ \sigma_d^2 = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty} |H_{\rm I}(f) |^2 \,\,{\rm d} {\it f}\hspace{0.05cm} = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty}{\rm si}^2(\pi f T)\,\,{\rm d} {\it f} = \frac{N_0}{2T } \hspace{0.05cm}.$$
+
:$$ \sigma_d^2 = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty} |H_{\rm I}(f) |^2 \,\,{\rm d} {\it f}\hspace{0.05cm} = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty}{\rm sinc}^2(f T)\,\,{\rm d} {\it f} = \frac{N_0}{2T } \hspace{0.05cm}.$$
:&nbsp; Here $H_{\rm I}(f)$ describes the integrator in the frequency domain.
+
:&nbsp; Here&nbsp; $H_{\rm I}(f)$&nbsp; describes the integrator in the frequency domain.
*With $E_{\rm B}= s_0^2 · T$ the same result is obtained:
+
*With&nbsp; $E_{\rm B}= s_0^2 · T$&nbsp; the same result is obtained:
 
:$$\frac{\sigma_d^2}{s_0^2} = \frac{N_0}{2 \cdot s_0^2 \cdot T } = \frac{N_0}{2 E_{\rm B} } = \frac{0.316}{2 } = 0.158\hspace{0.3cm}\Rightarrow \hspace{0.3cm} {\sigma_d}/{s_0} = 0.398 \approx 0.4 \hspace{0.05cm}.$$
 
:$$\frac{\sigma_d^2}{s_0^2} = \frac{N_0}{2 \cdot s_0^2 \cdot T } = \frac{N_0}{2 E_{\rm B} } = \frac{0.316}{2 } = 0.158\hspace{0.3cm}\Rightarrow \hspace{0.3cm} {\sigma_d}/{s_0} = 0.398 \approx 0.4 \hspace{0.05cm}.$$
  
  
  
'''(2)'''&nbsp; If the interfering participant uses the same M-sequence&nbsp; $(45)$&nbsp; as the considered user, <br>&nbsp; &nbsp; &nbsp; &nbsp; then the (normalized) detection utility samples are equal to&nbsp; $+2$&nbsp; $($at $25\%)$,&nbsp; $-2$&nbsp; $($at $25\%)$&nbsp; and&nbsp; $0$&nbsp; $($at $50\%)$.  
+
'''(2)'''&nbsp; If the interfering participant uses the same M-sequence&nbsp; $(45)$&nbsp; as the considered user, <br>&nbsp; &nbsp; &nbsp; &nbsp; then the&nbsp; (normalized)&nbsp; detection samples&nbsp; (without noise)&nbsp; are equal to&nbsp; $+2$&nbsp; $($at&nbsp; $25\%)$,&nbsp; $-2$&nbsp; $($at&nbsp; $25\%)$&nbsp; and&nbsp; $0$&nbsp; $($at&nbsp; $50\%)$.  
*When &nbsp;$d(νT) = ±2$,&nbsp; the error probability for the considered user is significantly reduced.&nbsp; In this case, both users transmit the same bit $($"$+1$" or "$-1$"$)$ and the distance from the threshold is doubled:
+
*When &nbsp;$d(νT) = ±2$,&nbsp; the error probability for the considered user is significantly reduced.&nbsp;  
 +
*In this case,&nbsp; both users transmit the same bit&nbsp; $($"$+1$"&nbsp; or&nbsp; "$-1$"$)$&nbsp; and the distance from the threshold is doubled:
 
:$$ p_{\rm B}\,\,\big [{\rm if}\,\, d (\nu T) = \pm 2s_0 \big ] = {\rm Q} \left ( 2 \cdot 2.515 \right ) = {\rm Q} \left ( 5.03 \right ) \approx 2.45 \cdot 10^{-7} \approx 0 \hspace{0.05cm}.$$
 
:$$ p_{\rm B}\,\,\big [{\rm if}\,\, d (\nu T) = \pm 2s_0 \big ] = {\rm Q} \left ( 2 \cdot 2.515 \right ) = {\rm Q} \left ( 5.03 \right ) \approx 2.45 \cdot 10^{-7} \approx 0 \hspace{0.05cm}.$$
*On the other hand, if &nbsp;$d(νT) = 0$&nbsp; (for example, if&nbsp; $a_\text{1(s)} = +1$&nbsp; and&nbsp; $a_\text{1(i)} = -1$&nbsp; holds or vice versa), the signals cancel completely and we obtain
+
*On the other hand,&nbsp; if &nbsp;$d(νT) = 0$&nbsp; (for example, if&nbsp; $a_\text{1(s)} = +1$ &nbsp; and&nbsp; $a_\text{1(i)} = -1$ &nbsp; holds or vice versa),&nbsp; the signals cancel completely and we obtain
:$$p_{\rm B}\,\,\big[{\rm if}\,\, d (\nu T) = 0 \big] = {\rm Q} \left ( 0 \right ) = 0.5 \hspace{0.05cm}.$$
+
:$$p_{\rm B}\big[{\rm if}\,\, d (\nu T) = 0 \big] = {\rm Q} \left ( 0 \right ) = 0.5 \hspace{0.05cm}.$$
*Averaging over these two equally probable possibilities, we thus obtain for the mean bit error probability:
+
*Averaging over these two equally probable possibilities,&nbsp; we thus obtain for the mean bit error probability:
 
:$$p_{\rm B}= 0.5 \cdot 2.45 \cdot 10^{-7}+ 0.5 \cdot 0.5 \hspace{0.15cm}\underline {\approx 25\%} \hspace{0.05cm}.$$
 
:$$p_{\rm B}= 0.5 \cdot 2.45 \cdot 10^{-7}+ 0.5 \cdot 0.5 \hspace{0.15cm}\underline {\approx 25\%} \hspace{0.05cm}.$$
  
  
  
'''(3)'''&nbsp;  We first consider only the useful part &nbsp; ⇒ &nbsp; $n(t) = 0$,&nbsp; restricting ourselves to the first data symbol and assuming the coefficient $a_\text{1(s)} = +1$.  
+
'''(3)'''&nbsp;  We first consider only the useful part &nbsp; ⇒ &nbsp; $n(t) = 0$,&nbsp; restricting ourselves to the first data symbol and assuming the coefficient&nbsp; $a_\text{1(s)} = +1$.  
 
*Then within this data bit&nbsp; $s(t) = c_{45}(t)$ holds.  
 
*Then within this data bit&nbsp; $s(t) = c_{45}(t)$ holds.  
*If the coefficient&nbsp; $a_\text{1(i)} $&nbsp; of the interfering participant is also&nbsp; $+1$, then for the signals specified in front in the time interval from&nbsp; $0$&nbsp; to&nbsp; $T$ we obtain:
+
*If the coefficient&nbsp; $a_\text{1(i)} $&nbsp; of the interfering participant is also&nbsp; $+1$,&nbsp; then for the signals specified in front in the time interval from&nbsp; $0$&nbsp; to&nbsp; $T$ we obtain:
 
:$$ r(t)  =  c_{45}(t) + c_{75}(t)\hspace{0.05cm},$$  
 
:$$ r(t)  =  c_{45}(t) + c_{75}(t)\hspace{0.05cm},$$  
 
:$$b(t)  =  r(t) \cdot c_{45}(t) = \left [c_{45}(t) + c_{75}(t) \right ] \cdot c_{45}(t) = 1+ c_{45}(t) \cdot c_{75}(t) \hspace{0.05cm},$$
 
:$$b(t)  =  r(t) \cdot c_{45}(t) = \left [c_{45}(t) + c_{75}(t) \right ] \cdot c_{45}(t) = 1+ c_{45}(t) \cdot c_{75}(t) \hspace{0.05cm},$$
 
:$$ d (T)  =  \frac{1}{T} \cdot \int_{0 }^{ T} b (t )\hspace{0.1cm} {\rm d}t = 1 + {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
 
:$$ d (T)  =  \frac{1}{T} \cdot \int_{0 }^{ T} b (t )\hspace{0.1cm} {\rm d}t = 1 + {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
*Here, &nbsp; $φ_\text{45, 75}(τ)$&nbsp; denotes the PCCF between the spreading sequences with octal identifiers&nbsp; $(45)$&nbsp; and&nbsp; $(75)$, which can be found in the table on the data page.
+
*Here, &nbsp; $φ_\text{45, 75}(τ)$&nbsp; denotes the PCCF between the spreading sequences with octal identifiers&nbsp; $(45)$&nbsp; and&nbsp; $(75)$,&nbsp; which can be found in the table on the data page.
 
+
*Correspondingly,&nbsp; for the detection sample without noise,&nbsp; given&nbsp; $a_\text{1(s)} = +1$&nbsp; and&nbsp; $a_\text{1(i)} =-1$:
*Correspondingly, for the detection utility sample, given&nbsp; $a_\text{1(s)} = +1$&nbsp; and&nbsp; $a_\text{1(i)} =-1$:
 
 
:$$d (T) = 1 - {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
 
:$$d (T) = 1 - {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
*For symmetry reasons, the coefficients&nbsp; $a_\text{1(s)}&nbsp; = -1$,&nbsp; $a_\text{1(i)} = -1$&nbsp; as well as&nbsp; $a_\text{1(s)} = -1$,&nbsp; $a_\text{1(i)} = +1$&nbsp; provide exactly the same contributions for the bit error probability as&nbsp; $a_\text{1(s)} = +1$,&nbsp; $a_\text{1(i)} = +1$&nbsp; and&nbsp; $a_{1(s)} = +1$,&nbsp; $a_{1(i)} = –1$ respectively, if we also consider the AWGN noise.
+
*For symmetry reasons,&nbsp; the coefficients&nbsp; $a_\text{1(s)}&nbsp; = -1$,&nbsp; $a_\text{1(i)} = -1$&nbsp; as well as&nbsp; $a_\text{1(s)} = -1$,&nbsp; $a_\text{1(i)} = +1$&nbsp; provide exactly the same contributions for the bit error probability as&nbsp; $a_\text{1(s)} = +1$,&nbsp; $a_\text{1(i)} = +1$&nbsp; and&nbsp; $a_{1(s)} = +1$,&nbsp; $a_{1(i)} = -1$ respectively,&nbsp; if we also consider the AWGN noise.
  
*Thus, using the result of subtask&nbsp; '''(1)'''&nbsp; and with&nbsp; $φ_\text{45, 75}(λ = 0) = 7/31$,&nbsp; we obtain approximately:
+
*Thus,&nbsp; using the result of subtask&nbsp; '''(1)'''&nbsp; and with&nbsp; $φ_\text{45, 75}(λ = 0) = 7/31$,&nbsp; we obtain approximately:
 
:$$p_{\rm B}  =  \frac{1}{2} \cdot {\rm Q} \left ( \frac{1+ 7/31}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{1- 7/31}{0.4} \right ) =  \frac{1}{2} \cdot {\rm Q} \left ( \frac{1.225}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{0.775}{0.4} \right ) = \frac{1}{2} \cdot {\rm Q} \left ( 3.06 \right ) + \frac{1}{2} \cdot {\rm Q} \left ( 1.94 \right )$$  
 
:$$p_{\rm B}  =  \frac{1}{2} \cdot {\rm Q} \left ( \frac{1+ 7/31}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{1- 7/31}{0.4} \right ) =  \frac{1}{2} \cdot {\rm Q} \left ( \frac{1.225}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{0.775}{0.4} \right ) = \frac{1}{2} \cdot {\rm Q} \left ( 3.06 \right ) + \frac{1}{2} \cdot {\rm Q} \left ( 1.94 \right )$$  
 
:$$ \Rightarrow \hspace{0.3cm} p_{\rm B}\approx  \frac{1}{2} \cdot \left [{\rm Q} \left ( 3 \right ) + {\rm Q} \left ( 2 \right ) \right ] = \frac{1}{2} \cdot \left [0.00135 + 0.02275 \right ] \hspace{0.15cm}\underline {= 1.2\%}\hspace{0.05cm}.$$
 
:$$ \Rightarrow \hspace{0.3cm} p_{\rm B}\approx  \frac{1}{2} \cdot \left [{\rm Q} \left ( 3 \right ) + {\rm Q} \left ( 2 \right ) \right ] = \frac{1}{2} \cdot \left [0.00135 + 0.02275 \right ] \hspace{0.15cm}\underline {= 1.2\%}\hspace{0.05cm}.$$
  
  
'''(4)'''&nbsp; Possible solutions are <u>2 and 3</u>:
+
 
 +
'''(4)'''&nbsp; Possible solutions are&nbsp; <u>2 and 3</u>:
 
* The PCCF value&nbsp; $φ_\text{45, 57}(λ = 0)$&nbsp; is only&nbsp; $1/31$&nbsp; in magnitude and thus the error probability is only slightly larger than&nbsp; $0.6\%$.  
 
* The PCCF value&nbsp; $φ_\text{45, 57}(λ = 0)$&nbsp; is only&nbsp; $1/31$&nbsp; in magnitude and thus the error probability is only slightly larger than&nbsp; $0.6\%$.  
*In contrast, the sequence with octal identifiers&nbsp; $(67)$&nbsp; leads to the same PCCF as sequence&nbsp; $(75)$.
+
*In contrast,&nbsp; the sequence with octal identifiers&nbsp; $(67)$&nbsp; leads to the same PCCF as sequence&nbsp; $(75)$.
*Without interfering participants, the following applies according to the data sheet: &nbsp; $p_{\rm B} = 0.6\%$.  
+
*Without interfering participants,&nbsp; the following applies according to the data sheet: &nbsp; $p_{\rm B} = 0.6\%$.  
*With interference, this value cannot be undercut &nbsp; ⇒  &nbsp; solution 1 is not possible.
+
*With interference,&nbsp; this value cannot be undercut &nbsp; ⇒  &nbsp; solution 1 is not possible.
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}

Latest revision as of 12:11, 17 February 2022

$\rm PACF$  and  $\rm PCCF$  of M-sequences with  $P = 31$

We consider  "Direct-Sequence Spread Spectrum Modulation"  with the following parameters:

  • The spreading is done with the M-sequence  $(45)_{\rm oct}$,  starting from the degree  $G = 5$.  The period length is thus  
$$P = 2^5 –1 = 31.$$
  • The AWGN parameter is set as  $10 · \lg \ (E_{\rm B}/N_0) = 5 \ \rm dB$    ⇒   $E_{\rm B}/N_0 = 3.162 = 1/0.316$.
  • The bit error probability without interfering subscribers in the same frequency band is:
$$p_{\rm B} = {\rm Q} \left ( \sqrt{ {2\cdot E_{\rm B}}/{N_{\rm 0}}}\right ) \approx {\rm Q} \left ( \sqrt{2 \cdot 3.162}\right ) = {\rm Q} \left ( 2.515 \right ) \approx 6 \cdot 10^{-3} \hspace{0.05cm}.$$
  • The value of the signal at sampling time, without noise or interfering subscribers, is equal to $±s_0$   ("Nyquist system").  As a result, the bit error probability for noise standard deviation  $σ_d$  before the decision stage $($ originating from the AWGN noise$)$  can be stated as follows:  
$$p_{\rm B} = {\rm Q} \left ( {s_0}/{\sigma_d}\right ) \hspace{0.05cm}.$$

In this exercise,  we want to investigate how the bit error probability is changed by an additional participant.

  • The possible spreading sequences of the interfering participant are also defined by  $P = 31$.  
  • The PN generators with octal identifiers  $(45)$,  $(51)$,  $(57)$,  $(67)$,  $(73)$ and  $(75)$ are available.
  • In the first column of the table the PCCF values for  $λ = 0$  are given, furthermore also the respective maximum value for another initial phase:
$$ {\rm Max}\,\,|{\it \varphi}_{45,\hspace{0.05cm}i}| = \max_{\lambda} \,\,|{\it \varphi}_{45,\hspace{0.05cm}i}(\lambda)| \hspace{0.05cm}.$$
  • The special case  $φ_\text{45, 45}(λ = 0)$  gives the PACF value of the spreading sequence with the octal identifier   $(45)$. 


In the course of this exercise and in the sample solution the following signals are mentioned:

 $q(t)$:   binary bipolar source signal,  symbol duration  $T$,
 $c(t)$:   $±1$ spreading signal,  chip duration  $T_c$,
 $s(t)$:   band-spread transmission signal;  it holds that  $s(t) = q(t) · c(t)$,  amplitude  $±s_0$,  chip duration  $T_c$,
 $n(t)$:   AWGN noise,  characterized by the quotient  $E_{\rm B}/N_0$,
 $i(t)$:   interference signal of the interfering subscriber,
 $r(t)$:   received signal; it holds that  $r(t) = s(t) + n(t) + i(t)$,
 $b(t)$:   band-compressed signal;  it holds that  $b(t)= r(t) · c(t)$,
 $d(t)$:   detection signal after integration of  $b(t)$  over the symbol duration  $T$,
 $v(t)$:   sink signal,  comparison with  $q(t)$  gives the error probability.



Notes:

$$ {\rm Q} (2) \approx 0.02275, \hspace{0.2cm}{\rm Q} (3) \approx 0.00135, \hspace{0.2cm}{\rm Q} (5) \approx 2.45 \cdot 10^{-7} \hspace{0.05cm}.$$


Questions

1

What is the  (normalized)  noise standard deviation at the decision maker?

$σ_d/s_0 \ = \ $

2

What is the bit error probability  $p_{\rm B}$ if the interfering participant  $i(t)$  uses the same M-sequence with octal identifier  $(45)$  as the participant under consideration?

$p_{\rm B}\ = \ $

$\ \%$

3

What is the approximate bit error probability  $p_{\rm B}$  if the interfering subscriber $i(t)$  uses the M-sequence with octal identifier  $(75)$? 

$p_{\rm B}\ = \ $

$\ \%$

4

What statements could possibly be made for a different spreading sequence of the interfering participant?

With octal identifier  $(51)$,    $p_{\rm B} = 0.1\%$   is possible.
With octal identifier  $(57)$,    $p_{\rm B} = 0.7\%$   is possible.
With octal identifier  $(67)$,    $p_{\rm B} = 1.2\%$   is possible.


Solution

(1)  From the two equations given above,  it follows directly:

$$p_{\rm B} = {\rm Q}(2.515) = {\rm Q}({s_0}/{\sigma_d}) \hspace{0.3cm}\Rightarrow\hspace{0.3cm} \frac{\sigma_d}{s_0} = \frac{1}{2.515} = 0.398 \hspace{0.15cm}\underline {\approx 0.4} \hspace{0.05cm}.$$
  • However,  one could also calculate this quantity using the more general equation
$$ \sigma_d^2 = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty} |H_{\rm I}(f) |^2 \,\,{\rm d} {\it f}\hspace{0.05cm} = \frac{N_0}{2 }\cdot\int^{+\infty}_{-\infty}{\rm sinc}^2(f T)\,\,{\rm d} {\it f} = \frac{N_0}{2T } \hspace{0.05cm}.$$
  Here  $H_{\rm I}(f)$  describes the integrator in the frequency domain.
  • With  $E_{\rm B}= s_0^2 · T$  the same result is obtained:
$$\frac{\sigma_d^2}{s_0^2} = \frac{N_0}{2 \cdot s_0^2 \cdot T } = \frac{N_0}{2 E_{\rm B} } = \frac{0.316}{2 } = 0.158\hspace{0.3cm}\Rightarrow \hspace{0.3cm} {\sigma_d}/{s_0} = 0.398 \approx 0.4 \hspace{0.05cm}.$$


(2)  If the interfering participant uses the same M-sequence  $(45)$  as the considered user,
        then the  (normalized)  detection samples  (without noise)  are equal to  $+2$  $($at  $25\%)$,  $-2$  $($at  $25\%)$  and  $0$  $($at  $50\%)$.

  • When  $d(νT) = ±2$,  the error probability for the considered user is significantly reduced. 
  • In this case,  both users transmit the same bit  $($"$+1$"  or  "$-1$"$)$  and the distance from the threshold is doubled:
$$ p_{\rm B}\,\,\big [{\rm if}\,\, d (\nu T) = \pm 2s_0 \big ] = {\rm Q} \left ( 2 \cdot 2.515 \right ) = {\rm Q} \left ( 5.03 \right ) \approx 2.45 \cdot 10^{-7} \approx 0 \hspace{0.05cm}.$$
  • On the other hand,  if  $d(νT) = 0$  (for example, if  $a_\text{1(s)} = +1$   and  $a_\text{1(i)} = -1$   holds or vice versa),  the signals cancel completely and we obtain
$$p_{\rm B}\big[{\rm if}\,\, d (\nu T) = 0 \big] = {\rm Q} \left ( 0 \right ) = 0.5 \hspace{0.05cm}.$$
  • Averaging over these two equally probable possibilities,  we thus obtain for the mean bit error probability:
$$p_{\rm B}= 0.5 \cdot 2.45 \cdot 10^{-7}+ 0.5 \cdot 0.5 \hspace{0.15cm}\underline {\approx 25\%} \hspace{0.05cm}.$$


(3)  We first consider only the useful part   ⇒   $n(t) = 0$,  restricting ourselves to the first data symbol and assuming the coefficient  $a_\text{1(s)} = +1$.

  • Then within this data bit  $s(t) = c_{45}(t)$ holds.
  • If the coefficient  $a_\text{1(i)} $  of the interfering participant is also  $+1$,  then for the signals specified in front in the time interval from  $0$  to  $T$ we obtain:
$$ r(t) = c_{45}(t) + c_{75}(t)\hspace{0.05cm},$$
$$b(t) = r(t) \cdot c_{45}(t) = \left [c_{45}(t) + c_{75}(t) \right ] \cdot c_{45}(t) = 1+ c_{45}(t) \cdot c_{75}(t) \hspace{0.05cm},$$
$$ d (T) = \frac{1}{T} \cdot \int_{0 }^{ T} b (t )\hspace{0.1cm} {\rm d}t = 1 + {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
  • Here,   $φ_\text{45, 75}(τ)$  denotes the PCCF between the spreading sequences with octal identifiers  $(45)$  and  $(75)$,  which can be found in the table on the data page.
  • Correspondingly,  for the detection sample without noise,  given  $a_\text{1(s)} = +1$  and  $a_\text{1(i)} =-1$:
$$d (T) = 1 - {\it \varphi}_{45,\hspace{0.05cm}75}(\lambda = 0) \hspace{0.05cm}.$$
  • For symmetry reasons,  the coefficients  $a_\text{1(s)}  = -1$,  $a_\text{1(i)} = -1$  as well as  $a_\text{1(s)} = -1$,  $a_\text{1(i)} = +1$  provide exactly the same contributions for the bit error probability as  $a_\text{1(s)} = +1$,  $a_\text{1(i)} = +1$  and  $a_{1(s)} = +1$,  $a_{1(i)} = -1$ respectively,  if we also consider the AWGN noise.
  • Thus,  using the result of subtask  (1)  and with  $φ_\text{45, 75}(λ = 0) = 7/31$,  we obtain approximately:
$$p_{\rm B} = \frac{1}{2} \cdot {\rm Q} \left ( \frac{1+ 7/31}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{1- 7/31}{0.4} \right ) = \frac{1}{2} \cdot {\rm Q} \left ( \frac{1.225}{0.4} \right ) + \frac{1}{2} \cdot {\rm Q} \left ( \frac{0.775}{0.4} \right ) = \frac{1}{2} \cdot {\rm Q} \left ( 3.06 \right ) + \frac{1}{2} \cdot {\rm Q} \left ( 1.94 \right )$$
$$ \Rightarrow \hspace{0.3cm} p_{\rm B}\approx \frac{1}{2} \cdot \left [{\rm Q} \left ( 3 \right ) + {\rm Q} \left ( 2 \right ) \right ] = \frac{1}{2} \cdot \left [0.00135 + 0.02275 \right ] \hspace{0.15cm}\underline {= 1.2\%}\hspace{0.05cm}.$$


(4)  Possible solutions are  2 and 3:

  • The PCCF value  $φ_\text{45, 57}(λ = 0)$  is only  $1/31$  in magnitude and thus the error probability is only slightly larger than  $0.6\%$.
  • In contrast,  the sequence with octal identifiers  $(67)$  leads to the same PCCF as sequence  $(75)$.
  • Without interfering participants,  the following applies according to the data sheet:   $p_{\rm B} = 0.6\%$.
  • With interference,  this value cannot be undercut   ⇒   solution 1 is not possible.