# 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
Figure 5 shows the comparison of resistance to sweep frequency interference effects. From the graph, it is clear that due to the high dynamism of sweep frequency interference, the resistance effect is not as pronounced as that against constant frequency interference. However, the BO_DSAC algorithm begins to converge after about 30 rounds, demonstrating better performance in terms of timeliness and accuracy compared to the Conv_DQN and DQN algorithms. It adapts more quickly and flexibly to the dynamic environment's impact on the information backhaul of cluster drones. Figure 6 presents a comparison of resistance to hybrid frequency interference effects. From the chart, it is evident that the BO_DSAC algorithm begins to gradually converge around 100 rounds and maintains a relatively stable resistance to interference after convergence, showing a more significant performance compared to other algorithms. Even though hybrid frequency interference is a challenging type of interference in real-world scenarios, and the high dynamism of the interference caused by \(\Delta r\) settings leads to an increased frequency of channel selection, which might reduce the reward results, the algorithm proposed in this article still offers a robust anti-interference effect. Figure 7 illustrates the impact of different hyperparameter sets on the BO_DSAC algorithm, conducted against a backdrop of hybrid frequency interference using Bayesian optimization. The optimized hyperparameters include the discount factor \(\gamma^{\mu*}\), learning rate \(lr^{\mu*}\), and target entropy \(\widetilde{H}^{*}\). The discount factor is used to determine the current value of future rewards. A higher discount factor makes the algorithm place more emphasis on future rewards, allowing the channel selection to achieve the best results over the entire task period. Conversely, a low discount factor will lead to \"short-sightedness\", ignoring beneficial long-term behaviors and increasing interference avoidance errors. The learning rate determines the speed of policy updates. An excessively high learning rate may cause instability during training, resulting in significant fluctuations in anti-interference performance. On the other hand, a low learning rate will slow down the learning process, making the anti-interference effects less noticeable and convergence slower. The target entropy is used to control the randomness of the policy. A high entropy value encourages the policy to explore more of the state space, maintaining channel stability while finding channels that successfully avoid interference. Conversely, a low entropy value makes the policy more deterministic, which can lead to local optima and unsatisfactory interference avoidance results. Therefore, appropriate hyperparameters can enhance the algorithm's ability to handle interference and improve the efficiency of relay communication in the drone cluster. To enhance the anti-interference performance throughout the entire task period and achieve efficient policy exploration, this paper implements dynamic control of the entropy coefficient, maintaining the entropy coefficient without exponential changes with training iterations. The initial set of hyperparameters is denoted as (0.9,1.0 \(\times\) 10\({}^{-4}\),10). According to [30], mainstream hyperparameter sets that have shown better performance are used for comparison experiments, denoted as (0.99, 5 \(\times\) 10\({}^{-4}\), 0.98), corresponding to the BO_DSAC_III and BO_DSAC_II curves in the figure. BO_DSAC_I represents the parameter set optimized by Bayesian optimization, denoted as (0.97, 5 \(\times\) 10\({}^{-4}\), 0.6). As shown in the figure, the parameter set optimized by Bayesian optimization exhibits a certain degree of improvement in anti-interference performance. Compared to the parameters during optimization and the nonoptimized parameter set, the total reward values increased by 7.1% and 3.3947 times, respectively. This demonstrates that the proposed algorithm can significantly enhance the problem-solving capability of the algorithm.