# MCST Scheme for UAV Systems over LoRa Networks
## 6 Numerical Simulations
### Simulation Results and Discussion  
Firstly, Table 2 describes the performance comparison between CSMA/LoRa, the proposed MCST/LoRa and mMCST/LoRa in terms of achievable BRF throughput, transmission latency and energy consumption for a single BRF transmission in the network with a fixed FRM payload size of 250 bytes. As we can observe that MCST can bring a higher achievable BRF throughput over CSMA in the LoRa network. Besides, MCST also shows its superiority in reducing the achievable transmission latency and energy consumption to CSMA-based LoRaWAN MAC. This is because MCST optimizes the transmission capacity by controlling the time fraction, which results in better throughput, lower latency and lower energy consumption. Figure 9 shows the performance comparison of three LoRaWAN MAC in terms of achievable BRF throughput and latency by varying FRM payload size in the 30-node random topology with the fixed basic rate of 200 kbps. We can observe that BRF throughput and transmission latency increase as the FRM payload size increases in the 30-node random topology. However, the simulation reveals that MCST/LoRa gives a higher achievable BRF throughput than CSMA/LoRa. Furthermore, the simulation results show that mMCST/LoRa provides much higher BRF throughput compared to MCST/LoRa. Quantitatively, when the size of FRM payload is 250 bytes in a 30-node random topology scenario, MCST/LoRa and mMCST/LoRa provide 4.75 Mbps and 4.85 Mbps of achievable BRF throughput, respectively, while CSMA/LoRa gives 4.41 Mbps. It means that MCST and mMCST can give 7.71% and 9.98% BRF throughput improvement than CSMA. The reason is MCST optimizes the transmission capacity which results in higher BRF throughput and lower transmission latency. In addition, reducing the control frame transmission time via mMCST gives benefits not only in improving BRF throughput but also in reducing transmission latency. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Achievable BRF** & **Achievable Transmission** & **Achievable Energy** \\  & **Throughput [kbps]** & **Latency [s]** & **Consumption [mJ]** \\ \hline CSMA/LoRa & 4.79 & 0.418 & 8.36 \\ \hline MCST/LoRa & 4.89 & 0.409 & 8.17 \\ \hline mMCST/LoRa & 4.92 & 0.407 & 8.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between CSMA, MCST, and mMCST over the LoRa network with single BRF transmission and a fixed frame payload size of 250 bytes. Table 3 describes the performance comparison in terms of energy consumption for the three LoRaWAN MAC. The result shows that MCST/LoRa can provide a better energy-efficient MAC than CSMA-based LoRaWAN MAC in the LoRa network 30-node random topology. This is because MCST/LoRa reduces the latency with the optimized transmission capacity through the MCST scheme and results in less energy consumption. However, the proposed mMCST/LoRa is the best energy-efficient MAC among the three MAC in this paper. Quantitatively, mMCST/LoRa can give up to 9.24% and 2.02% achievable energy consumption reduction compared to CSMA/LoRa and MCST/LoRa, respectively, when the FRM payload size is 250 bytes. The reason is mMCST/LoRa reduces the transmission latency by mitigating the control frame transmission time than MCST/LoRa. Figure 10 illustrates the comparison of achievable BRF throughput and latency when the number of nodes varies in the network with the fixed FRM payload size of 250 bytes. The results show that MCST can accomplish a higher BRF throughput regardless of the number of nodes compared to CSMA-approached LoRaWAN MAC. The BRF throughput decreases as the number of nodes increases because of the interference in the dense network. However, the optimal transmission capacity provided by the MCST scheme results in a better BRF throughput. Besides, the MCST scheme also outperforms in reducing the transmission latency, especially when the number of nodes is 240 in the network.