## 5 Experimental Results and Analysis
### Simulation Analysis of GA-DQN  
To verify the anti-jamming decision-making capability of the GA-DQN algorithm, this paper compares the GA-DQN algorithm with the conventional DQN algorithm [29], the Proximal Policy Optimization algorithm (PPO) [30], and the Q-learning algorithm [31]. For an objective analysis of the performance of the proposed algorithm, the parameters of the algorithms involved in the comparison were all set to the same values as those of the proposed algorithm. The maximum capacity of the experience replay buffer was 10,000, and the batch processing size was 64. The other simulation parameters were set as shown in Table 2. In addition, the normalized throughput of rewards was adopted as the evaluation index for the anti-jamming decision-making capability of the intelligent algorithm, and the sliding average method (with a window length of 50) was used for curve plotting to obtain smooth curves. In the performance comparison simulation, four distinct jamming environments were configured to analyze the anti-jamming performance and robustness of the proposed algorithm, as can be seen from Figure 7:  
* Single-jamming environment: The jammer employs only single comb-shaped sweeping jamming to disrupt the receiver;
* Composite-jamming environment: The jammer simultaneously deploys comb-shaped sweeping jamming and multi-channel jamming to challenge the receiver;
* Dynamic-jamming environment: The jammer switches jamming types every 100 time slots in the sequence of CSJ \(\rightarrow\) FH \(\rightarrow\) MTJ \(\rightarrow\) LSJ. This dynamic pattern prevents the DQN from fully exploring and adapting to any single jamming type;
* Intelligent-jamming environment: The jammer updates its target channels every 10 time slots, selecting the channels occupied by the communication signals in the previous 10 slots plus 4 adjacent channels. This adaptive strategy mimics intelligent adversarial behavior. In neural network training, the learning rate is of great importance to the performance of the neural network. To set appropriate learning rate parameters, this paper analyzed the performance of the DQN algorithm at different learning rates in a single-jamming environment. Figure 8 compares the normalized throughput performance of the algorithm proposed in this paper when the learning rates were 0.01, 0.001, and 0.0001, respectively. It can be seen from the figure that when the learning rate was 0.01, due to the too-fast convergence of the neural network parameters, the DQN calculation was unstable and problems such as gradient explosion occurred; when the learning rate was 0.0001, the convergence speed of the DQN network was slow and the ideal performance was not achieved; when the learning rate was set to 0.001, the DQN converged well and could achieve the ideal performance. Therefore, this learning rate was used in the subsequent simulations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length of communication time slot \(T_{s}\) & 350 \\ Number of channels \(M\) & 12 \\ Threshold of JSR \(\theta_{tt}\) & 8 \\ Discount rate \(\gamma\) & 0.3 \\ Communication power \(p_{L}/dBm\) & 10 \\ Jamming power \(p_{L}^{\prime}/dBm\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter settings. In this paper, the experience sample generation cycle of the GA-DQN algorithm was set to \(k\) time slots. To achieve optimal performance, it was necessary to validate the impact of different generation cycles K on the algorithm's performance. Figure 9 compares the normalized performance of the proposed algorithm in different generation cycles (\(k\in\{0,10,20,30,40,50\}\)) in a single-jamming environment.