# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 5 Simulation Analysis
### Simulation of Downlink RSMA System  
This paper validates the UAV-assisted communication downlink transmission optimization based on RSMA by MATLAB. The key parameters of the simulation are in Table 6. Assume that the upstream transmission bandwidth allocated to a user group is 0.5 MHz, and the upper limit of transmit power for a single user is 10 dBm.  
We first consider a scenario with a single user group (comprising two users) in the downlink system. In this case, the user group is allocated a bandwidth of 0.5 MHz and a total transmission power of 25 dBm. The rate curves for the users in the group are shown in Figure 5. As is illustrated, among the three mechanisms--OMA, NOMA, and RSMA--OMA achieves the smallest capacity region, while RSMA has the largest capacity region. This discrepancy arises because RSMA is equivalent to NOMA when no power is allocated to the common message for the user group. By allocating power to the common message appropriately, RSMA can achieve higher transmission rates than NOMA.  
Next, we examine how varying the number of users and the system's signal-to-noise ratio (SNR) impacts system throughput, as shown in Figure 6. All three access mechanisms utilize the optimization algorithm described in this paper to allocate power and bandwidth resources. The results indicate that as the number of users increases, the system throughput gradually approaches saturation. Additionally, higher system SNR results in increased throughput without any upper limit. Among the three approaches, the one presented in this paper demonstrates the best performance, with the RSMA strategy achieving 13.30% higher system throughput compared to the NOMA strategy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline System bandwidth & 10 MHz \\ Area size & \(500\times 500\times 75\) m\({}^{3}\) \\ UAV height & 75 m \\ Noise power spectral density & \(-174\) dBm/Hz \\ Range of Signal-to-noise ratio & 0–30 dB \\ Number of users & 10–130 \\ Users Distribution & Random distribution \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key parameters of downlink RSMA system simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **AFSA** & **M-AFSA** & **D-AFSA** & **MD-AFSA** \\ \hline F1 & 1.72 & 1.51 & 1.65 & 1.50 \\ F2 & 1.97 & 1.33 & 2.01 & 1.48 \\ F3 & 3.24 & 2.61 & 3.52 & 2.43 \\ F4 & 3.63 & 2.65 & 3.63 & 2.58 \\ F5 & 4.06 & 3.74 & 4.14 & 4.22 \\ F6 & 2.14 & 1.54 & 2.28 & 1.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Algorithm time characteristics (unit: s).  
By fixing the position of the UAV and allocating resources between and within user groups, we assess the downlink RSMA system throughput, as shown in Figure 7.  
In Figure 7, RSMA-A represents the optimization strategy proposed in this paper. RSMA-SP denotes a scenario where the UAV base station position is fixed, followed by resource allocation using the described strategy for both inter-group and intra-group resources. RSMA-SA-1 and RSMA-SA-2 represent situations where the resource allocations between and within groups are fixed (matching the optimal solution derived by RSMA-A), followed by the random selection of two UAV deployment positions for base station deployment. The results show that changing the UAV deployment position significantly  
Figure 5: Downstream user group rate profile.  
Figure 6: Variation of the throughput of downlink system with number of users and SNR.  
Figure 7: RSMA downlink system throughput under different strategies.  
impacts system throughput in the downlink system. This trend aligns with findings in the uplink system: when the UAV position is fixed and the resource allocation strategy is optimized, throughput can be effectively enhanced.