# Network Performance Optimization for Low-Voltage Power Line Communications
## 5 Simulation Results
### Network Simulation Results  
Simulation Environment and Results  
Programs such as SimPowerSystems and Matlab/Simulink can be used to simulate a power system distribution network. OMNeT++ and NS-2 are among the most popular tools used to simulate a communication network. In this work, co-simulation platforms designed for controlling information exchange between power and communication software tools can be used. Therefore, we study the LVPLC dynamic self-organization network in the simulation environment of OMNeT++4.0. Specifically, 30 terminal stations are placed in an 800 \(\times\) 800 m\({}^{2}\) home local area on the secondary side of the any one phase power distribution grid. This study designs a typical physical topology and a logical communication topology, which are shown in Figure 7a,b, respectively. In Figure 7a, the distance between the neighboring stations is approximately 20 m. The polygonal line symbol shows a line that is 150 m long. Stations communicate with each other within a 200 m range. Outside this range, the stations cannot communicate effectively. Therefore, the logical communication topology can be obtained as shown in Figure 7b. The red station depicts the CCo, while the blue stations present the proxies and terminal stations. The station that uses the contour dotted line shows the new associated stations. Table 1 lists the network's actual measured parameter. Figure 8a-c shows the topology structure after network in the asymmetric/symmetric channel. The channel asymmetric properties influence the topology structure. Some parts of the stations' topology measurement (distribution of distances, closeness centrality, and betweenness centrality) are similar to some extent. The node[0], node[2], and node[8] measurement results are presented in Table 2 to quantitatively demonstrate the importance of the stations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distribution of** & **Node Index** & **Degree Centrality** & **Closeness Degree** & **Betweenness Centrality** & **Clustering Coefficient** \\ \hline  & 0 & 4 & 0.329 & 0.09 & 0.526 \\
3.69 & 2 & 7 & 0.372 & 0.321 & 0.381 \\  & 8 & 7 & 0.363 & 0.165 & 0.476 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nodes’ measurement comparison results. Figure 8: Improved LVPLC artificial cobweb topology. (**a**) Topology in the uplink/downlink cr of 96% and 93%; (**b**) topology in the uplink/downlink cr of 93% and 96%; and (**c**) topology in the cr of 96% for the symmetric channel. Figure 7: Typical scenario topology. (**a**) Typical scenario physical topology; (**b**) Typical scenario logical topology. Similarly, we can reduce the 14.6% at a maximum, increase the 8.7% on the average and basically keeps the minimum to the same level using the proposed approach, instead of the \(Q\)-learning under the circumstances of uplink 93% success ratio and downlink 96% success ratio. Finally, this paper explains the comparison results of the average end to end delay of node[8] (Figure 10c). Compared to the symmetric channel-based \(Q\)-learning featuring a success ratio of 96%, the proposed method performs about 5.3% above maximum value and a minimum basically the same, while reducing the average by 21% under the traffic environment of uplink 96% success ratio and 93% downlink success ratio. Similarly, we can reduce the 0.63% at a maximum, increase the 5.24% on the average and basically keeps the minimum to the same level using the proposed approach instead of the \(Q\)-learning under the circumstances of uplink 93% success ratio and downlink 96% success ratio. Number of the Average Hops in the Coverage Stage Analysis  
Figure 11a explains the results of the number of the average hops to CCo (node[0]). Compared to the symmetric channel-based \(Q\)-learning featuring a success ratio of 96%, the proposed method performs approximately 21.3% above the maximum value and obtains a minimum that is basically the same while increasing the average by 16.3% under the traffic environment of uplink 96% vacc ratio and a 93% downlink success ratio.