# Exercise 3.2: Eye Pattern according to Gaussian Low-Pass

Basic transmission pulse  $g_s(t)$  ⇒   blue curve,
basic detection pulse  $g_d(t)$   ⇒   red curve

Let a binary bipolar redundancy-free baseband system with bit rate  $R_{\rm B} = 100\,{\rm Mbit/s}$  be given.  Further:

• Rectangular transmission pulses,  possible amplitude values  $± 1\,{\rm V}$.
• The AWGN noise power density  $($on the resistor  $1 \, \Omega)$:  $N_0=10^{\rm -9} \, {\rm V}^2/{\rm Hz}$.
• Receiver filter is a Gaussian low-pass with cutoff frequency  $f_{\rm G} = 50 \, {\rm MHz}$.  The frequency response is:
$$H_{\rm G}(f) = {\rm e}^{- \pi \hspace{0.05cm}\cdot \hspace{0.05cm}{f}^2/({2f_{\rm G}})^2} \hspace{0.05cm}.$$
• The basic detection pulse  $g_d(t) = g_s(t) * h_{\rm G}(t)$  is shown in the graph (red curve).
Some prominent pulse sample values are indicated.
• The detection noise power can be calculated using the following equation:
$$\sigma_d^2 = {N_0}/{2} \cdot \int_{-\infty}^{+\infty} |H_{\rm G}(f)|^2 \,{\rm d} f \hspace{0.05cm}.$$

The  "eye diagram"  can be used to determine the bit error probability  $($in general:  symbol error probability$)$.

1. The mean symbol error probability  $p_{\rm S}$  is obtained from this after averaging over all possible noiseless detection samples in consideration of the noise rms value  $\sigma_d$.
2. The worst-case error probability serves as an upper bound for  $p_{\rm S}$:
$$p_{\rm U} = {\rm Q} \left( \frac{\ddot{o}(T_{\rm D})/2}{ \sigma_d} \right) \hspace{0.3cm}{\rm with}\hspace{0.3cm}\frac{\ddot{o}(T_{\rm D})}{ 2}= g_d(t=0) - |g_d(t=T)|- |g_d(t=-T)|-\hspace{0.01cm}\text{ ...}$$
Here,  $\ddot{o}(T_{\rm D})$  denotes the vertical eye opening.  Let the detection time  $T_{\rm D} = 0$  be optimally chosen.

Notes:

### Questions

1

What is the symbol duration?

 $T \ = \$ $\ {\rm ns}$

2

What is the rms value of the detection noise signal?

 $\sigma_d\ = \$ $\ {\rm V}$

3

What are the basic detection pulse samples  $g_{\rm \nu} = g_d(\nu \cdot T)$,  in particular

 $g_0\ = \$ $\ {\rm V}$ $g_1\ = \$ $\ {\rm V}$ $g_2\ = \$ $\ {\rm V}$

4

Calculate the eye opening and the worst-case error probability.

 $\ddot{o}(T_{\rm D})\ = \$ $\ {\rm V}$ $p_{\rm U}\ = \$ $\ \cdot 10^{\rm -3}$

5

Calculate the average error probability  $p_{\rm S}$  by averaging over the possible noiseless samples.

 $p_{\rm S}\ = \$ $\ \cdot 10^{\rm -3}$

6

What would be the minimum increase in transmitted pulse amplitude  $s_0$  required to satisfy the condition  $p_{\rm S} \ ≤ 10^{\rm -10}$?

 $s_0\ = \$ $\ {\rm V}$

### Solution

#### Solution

(1)  The symbol duration is the reciprocal of the bit rate:

$$T = \frac{1}{10^8\,{\rm bit/s}} = 10^{-8}\,{\rm s}\hspace{0.15cm}\underline { = 10\,{\rm ns}} \hspace{0.05cm}.$$

(2)  Integration according to the given equation leads to:

$$\sigma_d^2 = \frac{N_0}{2} \cdot \int_{-\infty}^{+\infty} |H_{\rm G}(f)|^2 \,{\rm d} f = \frac{N_0 \cdot f_{\rm G}}{\sqrt{2}}= \frac{10^{-9}\,{\rm V/Hz} \cdot 5 \cdot 10^{7}\,{\rm Hz} }{\sqrt{2}}\approx 0.035\,{\rm V^2}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}\sigma_d \hspace{0.15cm}\underline { = 0.188\,{\rm V}}\hspace{0.05cm}.$$

(3)  These values can be obtained from the graph:

$$g_0 = g_d(0)\hspace{0.15cm}\underline { = 0.790\,{\rm V}}, \hspace{0.2cm}g_1 = g_d(10\,{\rm ns}) \hspace{0.15cm}\underline {= 0.105\,{\rm V}}= g_{-1}, \hspace{0.2cm}g_2 = g_{-2} \hspace{0.15cm}\underline { \approx 0} \hspace{0.05cm}.$$

(4)  Using the basic detection pulse values calculated in  (3),  we obtain for the vertical eye opening:

$$\ddot{o}(T_{\rm D}) = 2 \cdot (g_0 - g_1 - g_{-1}) = 2 \cdot (0.790\,{\rm V} - 2\cdot 0.105\,{\rm V}) \hspace{0.15cm}\underline {= 1.16\,{\rm V}}\hspace{0.05cm}.$$
Eye diagram with and without noise
• The right graph shows the noisless eye diagram.  One can see from this the vertical eye opening in the center of the symbol:
$$\ddot{o}(T_{\rm D} = 0) = 2 \cdot 0.58 \cdot s_0.$$
• Together with the noise rms value,  we obtain for the worst-case error probability:
$$p_{\rm U} = {\rm Q} \left( \frac{1.16\,{\rm V}/2}{ 0.188\,{\rm V}} \right) \approx {\rm Q}(3.08)\hspace{0.15cm}\underline {\approx 10^{-3}} \hspace{0.05cm}.$$

(5)  From the noisless eye diagram,  one can see that the signal component at the detection time  $T_{\rm D} = 0$  can assume six different values.

• In the upper half of the eye,  these are:
$$1.)\hspace{0.2cm} g_0 + g_1 + g_{-1} = 0.790\,{\rm V} + 2\cdot 0.105\,{\rm V}= 1\,{\rm V} = s_0$$
$$\Rightarrow \hspace{0.3cm} p_{\rm 1} = {\rm Q} \left( \frac{1\,{\rm V}}{ 0.188\,{\rm V}} \right) \approx 5 \cdot 10^{-8} \hspace{0.05cm},$$
$$2.)\hspace{0.2cm} g_0 = 0.790\,{\rm V} \hspace{0.3cm}\Rightarrow \hspace{0.3cm} p_{\rm 2} = {\rm Q} \left( \frac{0.790\,{\rm V}}{ 0.188\,{\rm V}} \right) \approx 1.3 \cdot 10^{-5} \hspace{0.05cm},$$
$$3.)\hspace{0.2cm} g_0 - g_1 - g_{-1} = 0.580\,{\rm V} = \ddot{o}(T_{\rm D})/2\hspace{0.3cm}\Rightarrow \hspace{0.3cm}p_{\rm 3} = p_{\rm U} \approx 10^{-3} \hspace{0.05cm}.$$
• Averaging over these values with appropriate weighting  $(p_2$  occurs twice as often as  $p_1$  and  $p_3)$  gives:
$$p_{\rm S} \ = \ {1}/{4} \cdot (p_{\rm 1} + 2 \cdot p_{\rm 2} + p_{\rm 3}) = {1}/{4} \cdot (5 \cdot 10^{-8} + 2 \cdot 1.3 \cdot 10^{-5} + 10^{-3}) \hspace{0.15cm}\underline { \approx 0.256 \cdot 10^{-3}} \hspace{0.05cm}.$$
• Since  $p_1$  and  $p_2$  are much smaller than  $p_3 = p_{\rm U}$,  the average error probability is (almost) a factor of  $4$  smaller than  $p_{\rm U}$.

(6)  To reduce the error probability,  $s_0$  must be increased.  Thus,  the approximation  $p_{\rm S} ≈ p_{\rm U}/4$  is even more accurate:

$$p_{\rm S} \le 10^{-10}\hspace{0.3cm}\Rightarrow \hspace{0.3cm}p_{\rm U} = {\rm Q} \left( \frac{0.58 \cdot s_0}{ 0.188\,{\rm V}} \right)\le 4 \cdot 10^{-10}\hspace{0.3cm} \Rightarrow \hspace{0.3cm} \frac{0.58 \cdot s_0}{ 0.188\,{\rm V}} \ge 6.15 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}s_0 \ge 1.993\,{\rm V} \hspace{0.15cm}\underline { \approx 2\,{\rm V}} \hspace{0.05cm}.$$