# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 4 Simulation and Comparison
The result shows that when decoding performance of \(S_{1}\) increases, the performance of related \(S_{2}\) will decrease, that is to say, the performance increase of one source is on the price of performance decrease of another source. Therefore, we only have to set desired parameters, and the scheme proposed in this paper will then satisfy different needs. \begin{table}
\begin{tabular}{c c c} \hline
**Importance Levels** & **Network Coding Rule** & **Selection Weights** & **Degree Distributions** \\ \hline \multirow{6}{*}{\(y_{i,1,2}=y_{i,2,2}\)} & \(p_{1}=0.15\) & \(w_{1,1}=0.923\) & \(\Psi_{1}(x)=\Psi_{2}(x)=0.0523x+0.07433x^{2}+0.0174x^{3}\) \\  & \(p_{1}=0.15\) & \(w_{1,2}=0.077\) & \(+0.0124x^{4}+0.0146x^{5}+0.0194x^{6}+0.0237x^{8}\) \\  & \(p_{2}=0.15\) & \(w_{2,1}=0.923\) & \(+0.0142x^{10}+0.0237x^{12}+0.0196x^{16}+0.0069x^{18}\) \\  & \(p_{3}=0.70\) & \(w_{2,1}=0.923\) & \(+0.0068x^{19}+0.0306x^{22}+0.038x^{28}+0.0515x^{36}\) \\  & & \(w_{2,2}=0.077\) & \(+0.041x^{45}+0.01547x^{50}\) \\ \hline \multirow{6}{*}{\(y_{i,1,2}=10^{4}y_{i,2,2}\)} & \multirow{6}{*}{\(p_{1}=0.178\)} & \(\Psi_{1}(x)=0.059x+0.484x^{2}+0.0216x^{3}+0.0196x^{4}\) \\  & & \(+0.0087x^{6}+0.0074x^{2}+0.0222x^{8}+0.019x^{11}\) \\  & & \(+0.014x^{41}+0.022x^{19}+0.0335x^{22}+0.0384x^{30}\) \\ \cline{1-1}  & \(p_{1}=0.178\) & \(w_{1,1}=0.860\) & \(+0.0355x^{37}+0.0387x^{4}+0.0174x^{49}+0.146x^{50}\) \\ \cline{1-1}  & \(p_{2}=0.122\) & \(w_{2,1}=0.923\) & \(\Psi_{2}(x)=0.045x+0.4637x^{2}+0.0156x^{3}\) \\ \cline{1-1}  & \(p_{3}=0.700\) & \(w_{2,1}=0.952\) & \(+0.0104x^{4}+0.0247x^{5}+0.0357x^{6}+0.0197x^{8}\) \\ \cline{1-1}  & & \(+0.0175x^{10}+0.011x^{12}+0.0286x^{14}+0.034x^{19}\) \\ \cline{1-1}  & & \(+0.031x^{25}+0.024x^{29}+0.052x^{35}+0.028x^{41}\) \\ \cline{1-1}  & & \(+0.0229x^{39}+0.137x^{50}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Optimal network coding rule, selection weights and degree distributions for DURC with different importance levels. Figure 5: Decoding error rate versus the total overhead at the destination: (**a**) various DRC schemes; and (**b**) various UEP schemes. We also estimate the throughput performance of the DURC scheme. To substitute the parameter setting in this section into (10), we can derive the system throughput upper bound as  
\[thr\_up=\left\{ \begin{aligned} &\frac{5}{6}\,& 0\leq\varepsilon_{RD}\leq\frac{1}{3},\\ &\frac{1+(1-\varepsilon_{RD})}{2}\,&\frac{1}{3}< \varepsilon_{RD}\leq 1.\end{aligned} \right. \tag{15}\]  
Figure 7 shows the system throughput versus erasure probability of channel \(R-D\) for information in one source and eep-DURC. To match the system setting of primary link and secondary link, we set \(\varepsilon\leq 0.5\). The result in Figure 7 demonstrated the same trend of Figure 5(a), where the throughput of MIB is much higher than eep-DURC, but the throughput of LIB is decreased. However, for the systems that need higher protection of MIB, this tradeoff is meaningful. Figure 6: Decoding performance of the DURC schemes: (**a**) DER performance of single node; (**b**) DER performance of various UEP setups. Figure 7: System throughput versus erasure probabilities.