1. **Problem Statement:**
We have a rate-\(\frac{1}{2}\) convolutional code with constraint length 4.
The first generator polynomial \(h_1\) is given by 10000 and the second generator polynomial \(h_2\) is 01011.
The input sequence is \(\{1 \quad 0 \quad 1 \quad 1 \quad 0 \quad 1 \quad 0 \quad 1 \quad 1 \quad 0\}\).
2. **Understanding the Code:**
- Rate \(\frac{1}{2}\) means for each input bit, two output bits are generated.
- Constraint length 4 means the encoder has 4 memory elements (including the current input bit).
- The generator polynomials define how the input bits and memory bits are combined to produce output bits.
3. **Generator Polynomials:**
- \(h_1 = 10000\) means the first output bit depends only on the current input bit (the first '1') and no memory bits.
- \(h_2 = 01011\) means the second output bit depends on the second, fourth, and fifth bits in the shift register.
4. **Shift Register Setup:**
We consider the input bit and 3 previous bits (total 4 bits) for convolution.
Since \(h_1\) and \(h_2\) are 5 bits long, we assume the first bit corresponds to the current input, and the next 4 bits correspond to memory bits.
5. **Input Sequence with Zero Padding:**
To handle initial memory, pad input with 4 zeros at the start:
\[
\{0,0,0,0,1,0,1,1,0,1,0,1,1,0\}
\]
6. **Calculate Output Bits:**
For each input bit starting from index 4 (the first actual input), calculate two output bits:
- \(y_1[n] = \sum_{i=0}^4 h_1[i] \cdot x[n-i] \mod 2\)
- \(y_2[n] = \sum_{i=0}^4 h_2[i] \cdot x[n-i] \mod 2\)
Since \(h_1 = 1 0 0 0 0\), only \(x[n]\) matters for \(y_1[n]\).
For \(h_2 = 0 1 0 1 1\), \(y_2[n] = x[n-1] + x[n-3] + x[n-4] \mod 2\).
7. **Step-by-step Calculation:**
Indexing \(n\) from 4 to 13 (10 input bits):
| n | x[n] | y_1[n] = x[n] | y_2[n] = x[n-1]+x[n-3]+x[n-4] mod 2 |
|---|-------|--------------|------------------------------------|
| 4 | 1 | 1 | 0+0+0=0 |
| 5 | 0 | 0 | 1+0+0=1 |
| 6 | 1 | 1 | 0+1+0=1 |
| 7 | 1 | 1 | 1+0+1=0 |
| 8 | 0 | 0 | 1+1+1=1 |
| 9 | 1 | 1 | 0+1+0=1 |
|10 | 0 | 0 | 1+0+1=0 |
|11 | 1 | 1 | 0+1+0=1 |
|12 | 1 | 1 | 1+0+1=0 |
|13 | 0 | 0 | 1+1+0=0 |
8. **Output Sequence:**
Concatenate \(y_1[n]\) and \(y_2[n]\) for each \(n\):
\[
\{(1,0), (0,1), (1,1), (1,0), (0,1), (1,1), (0,0), (1,1), (1,0), (0,0)\}
\]
Or as a single sequence:
\[
1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,1,1,0,0,0
\]
**Final answer:** The output sequence is \(\{1,0,0,1,1,1,1,0,0,1,1,1,0,0,1,1,1,0,0,0\}\).
Convolutional Code 38411A
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