## 4 Simulations and Analyses  
In our simulation analyses, we mainly focus on how the communication system, when faced with a random sweeping jamming attack with the change in jamming parameters, can extract the learned experience from previously learned experiences using the proposed Meta-PPO algorithm and quickly select the best channel. As shown in Figure 8, it is assumed that the jammer launches random sweep jamming attacks. Within each jamming cycle, the jammer randomly chooses its transmission power, then sweeps all channels within frequency band \(B\), and randomly selects up to three channels to launch jamming attacks. The proposed algorithm is then verified through computer simulations based on a neural network built with Pytorch. Table 2 shows the parameters used in our simulations. In the simulations, the proposed Meta-PPO algorithm implemented its learning processes for 1000 episodes and 10 iterations for parameter updates in each episode. Figure 8: Random sweep frequency jamming’s channel–time slot diagram. Figure 9 shows the curves of normalized network throughput changing with the episodes when the proposed Meta-PPO algorithm was used, which was compared with PPO [31], DQN [32], Double DQN [33], and Dueling DQN [34] algorithms. Normalized throughput is a widely accepted criterion for measuring the performance of an agent's strategy [35]. A better strategy the agent takes will result in higher normalized network throughput. As can be seen from Figure 9, for the proposed Meta-PPO algorithm, the normalized network throughput rises rapidly in the initial stage, before the 50 th episode, and then reaches its maximum value after it. This indicates that in the early stages of the learning process, the agent can quickly reach an effective strategy to deal with the jamming attacks. However, between the 100 th and 150 th episodes, the normalized network throughput drops slightly, suggesting that the Meta-PPO algorithm undergoes a fine-tuning or policy oscillation stage. After the 200 th episode, the normalized network throughput curve becomes flat, showing that there is little change in the subsequent learning process. This indicates that the proposed Meta-PPO algorithm has found an efficient strategy and further optimization after this point brings few gains in the normalized network throughput. In contrast, for PPO, DQN, Double DQN, and Dueling DQN algorithms, it takes them more than 300 episodes to research the best strategy. This shows that the proposed Meta-PPO algorithm can quickly achieve similar or even better anti-jamming strategies for wireless communication systems using a shorter period of learning time compared with traditional deep reinforcement learning-based algorithms. The Meta-PPO algorithm saves 60% of the time in strategy convergence compared to the PPO algorithm, and nearly 80% of the time compared to the DQN algorithm. This simulation result shows the better performance of our proposed Meta-PPO algorithm in terms of rapid adaptation ability for anti-jamming tasks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Numerical Values** & **Parameters** & **Numerical Values** \\ \hline PPO Reward discount rate \(\gamma\) & 0.95 & Policy update cut rate in PPO & 0.2 \\ Estimate the strategic advantage & 1 & The weight of the median loss of & 0.5 \\ discount factor & & the PPO cost function & 0.5 \\ Size of the hidden layer of the policy & 100 & Weight of the entropy & \\ network & & regularization term in PPO & 0.01 \\ Batch size for each task \(T_{i}\) & 5 & Number of channels \(N_{0}\) & 5 \\ MAML gradient update learning rate \(\alpha\) & 0.1 & Total channel bandwidth \(B\) & 10 MHZ \\ Total number of batches learned & 1000 & Channel switching loss factor \(C_{f}\) & \(10^{-4}\) \\ Number of jamming channels \(K_{T}\) & 3 & Number of tasks per batch & 10 \\ Jamming power \(p_{k}^{\prime}\) & [10 dBm,15 dBm,20 dBm] & Transmission power \(p_{n}\) & [5 dBm,10 dBm,15 dBm] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters.