# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 4 Simulations
### Simulation Results  
We generate different network topologies when performing the first scenario. Whenever executing each simulation, a source node produces 100 packets per second and the simulation time is 300 s, so a sender creates 30,000 packets during each simulation. For each experiment, we measure total packets received by each multi-receiver and compute the average number of received packets. These simulation results are represented in Figure 4. This graph shows the average number of packets received by multi-receivers during the simulation time. As seen from this graph, the performance of the proposed \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of channels used & 11 \\ Network size & 30 nodes over 900 m \(\times\) 900 m \\ Transmission range & 250 m \\ Transmission rate at a physical layer & 11 Mbits/s \\ Physical layer protocol & IEEE PHY 802.11b \\ Multicast routing protocol & MOSPF \\ Packet size & 512 bytes \\ Transmission rate at an application layer & 100 packets/s \\ Traffic model & MCBR \\ Simulation time & 300 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. MICA algorithm is much better than that of MCM. The average number of packets received by multi-receivers using MICA is approximately between 25,000 and 29,000. This value is very close to the number of packets (30,000) the source generates. On the other hand, the majority of the average number of packets using MCM is below 20,000 and the worst case is under 5000. We also measured the standard deviation of all packets received by multi-receivers. The result is shown in Figure 5. This picture represents the standard deviation of the packets received by receivers. The standard deviation of MICA is much lower than that of MCM. For 10 experiments, the standard deviation of MICA was below 2000, which means all receivers receive packets evenly. However, MCM has a much higher value than MICA, which means some destinations receive some amount of packets but other nodes cannot receive a certain amount of packets. The standard deviation in our simulation shows the variability of packets received by all multi-receivers. If the value of standard deviation is small, all multi-receivers fairly receive data packets, but a large value depicts some receivers obtain enough packets, but others do not. Consequently, if the variance of packets received by multi-receivers is extremely large, then it indicates that overall network throughput is not outstanding. Finally, we calculate the average number of packets received in simulations with a different number of multi-receivers by assigning the number of receivers to 2, 4, 6, 8, and 10. The results are shown in Figure 6. This graph shows the trend of the average packets received as the number of receivers is increased. Although the number of receivers is increased, the performance of MICA is  
Figure 4: Average packets received by multi-receivers. Figure 5: Standard deviation of all packets received by multi-receivers. stable. All receivers receive almost all of the packets from the source regardless of the number of receivers. However, the performance of MCM is affected by the number of multi-receivers because the chance of network interference is also increased. We also compute the average end-to-end packet delay of MICA and MCM by comparing the average time for each packet takes to arrive at multi-receivers. Figure 7 shows the trend of average end-to-end packet delay. The average delay of MICA is almost same regardless of the number of receivers. However, the delay of MCM gets higher as the number of receivers is increases because of network interference. If network interference exists then it causes packet collision and retransmission, so the end-to-end delay is also increased. The most important reason why we get these simulation results is that the MCM algorithm has HCP, which we discussed in Section 2. We conclude that HCP yields network interference among wireless nodes. As a result, it causes a poor network throughput.