# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate
### Optimization of Power Allocation Factor for Cluster Drones
These experiments allowed for observing the channel selection performance of the relay drone and the optimization of the total communication rate. It is assumed that the formation spacing of cluster drones is 200m, and their information backhaul tasks are performed within a three-dimensional space of 3500 m \(\times\) 3500 m \(\times\) 100 m. Table 2 lists the experimental parameters set for this study. ### Verification of the BO_DSAC Algorithm's Effectiveness

The purpose of the first set of experiments was to verify whether the relay drone's channel selection using the BO_DSAC algorithm can effectively avoid external malicious interference. The types of interference set in this study included constant frequency interference, sweep frequency interference, and hybrid frequency interference. The number of channels affected by the constant frequency interference was \(F/2\), the number of sweep frequency interference channels was \(F/2\), and each cycle moved down six channels in the decreasing frequency direction. The hybrid frequency interference consisted of constant frequency interference on three channels combined with sweep frequency interference that moved down three channels each cycle in the decreasing frequency direction. To verify the effectiveness and superiority of the algorithm presented in this paper, it is compared with the Convolutional Neural Network-Enhanced DQN (Conv_DQN) algorithm, the standard DQN algorithm, and random selection. To ensure fairness, the three algorithms were set with the same number of training sessions \((num\_episodes=1000)\), number of interactions \((MDPL=200)\), network layers \((N^{n}=256)\), learning rate \((lr^{u}=5\times 10^{-4})\), discount factor \((\gamma^{u}=0.97)\), and training batch size \((batch\_size=48)\). Figures 4-6 respectively show the anti-interference effects of the three types of interference. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(T\) & \(100\) & \(M\) & \(16\) \\ \((x^{l},y^{l},0)\) & \((1500,1500,0)\) & \(G_{l}G_{r}\) & \(20\) \\ \(F_{f}^{Rauv}\) & \([1.95\;\mathrm{GHz},2.05\;\mathrm{GHz}]\) & \(B_{n}^{Rauv}\) & \(10\;\mathrm{Mbps}\) \\ \(pF\) & \(30\;\mathrm{dBm}\) & \(H_{1}^{*}\) & \(13\;\mathrm{dB}\) \\ \(H_{l}\) & \(30\;\mathrm{m}\) & \(H_{h}\) & \(100\;\mathrm{m}\) \\ \(P_{max}^{R}\) & \(40\;\mathrm{dBm}\) & \(F_{max}^{Rauv}=F_{max}^{Fauv}\) & \(5\times 10^{4}\;\mathrm{J}\) \\ \(V_{max}^{over}\) & \(1\;\mathrm{m/s}\) & \(R_{l}\) & \(40\;\mathrm{Mbps}\) \\ \(n_{0}\) & \(-150\;\mathrm{dBm/Hz}\) & \(pl\) & \(8000\;\mathrm{W}\) \\ \(p^{L}=p^{L}\) & \(0.5\) & \(V_{max}^{over}\) & \(12\;\mathrm{m/s}\) \\ \(F\) & \(10\) & \(BER_{q}\) & \(10^{-6}\) \\ \(D_{n}\) & \(-90\;\mathrm{dBm}\) & \(mg\) & \(19.6\;\mathrm{N}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameters. Figure 4: Comparison of resistances to Constant frequency interference effects. Figure 5: Comparison of resistance to sweep frequency interference effects. Figure 6: Comparison of resistance to hybrid frequency interference effects. Figure 4 illustrates the anti-interference performance of the BO_DSAC algorithm under a constant frequency interference scenario. From the graph, it is evident that the BO_DSAC begins to converge gradually between 70 and 100 training rounds, achieving maximum reward values upon convergence. Although the constant frequency interference model is relatively simple, the algorithm demonstrates optimal performance, profoundly reflecting the superiority of the approach proposed in this paper. The introduction of entropy in the BO_DSAC algorithm leads to occasional fluctuations in reward values across some rounds; however, this also confirms that the BO_DSAC maintains a robust search for solutions throughout the training process, preventing it from settling into local optima. The graph also shows that compared to the Conv_DQN and DQN algorithms, the proposed algorithm converges faster and exhibits more pronounced resistance to interference.