## 5 Experimental Results and Analysis
### Experiment on the Generalization of Network Application  
Intelligent anti-jamming methods based on Deep Reinforcement Learning (DRL) determine optimal communication anti-jamming strategies autonomously by learning the patterns and characteristics of detected jamming signals, thus evading jamming signals emitted by jammers. However, a distinctive feature of such methods is that they require acquiring jamming information over sufficient time slots for strategy exploration. In contrast, the intelligent anti-jamming decision-making method based on the GA-DQN proposed in this paper directly leverages a GAN to learn the time-frequency characteristics of sudden jamming signals from short-term attacks. It then generates samples with similar characteristics as the jamming signals. When facing unfamiliar and unknown jamming, this enables the DQN to converge more rapidly. Compared with traditional intelligent anti-jamming methods, it exhibits stronger generalization. The following presents an experimental investigation into the application effects of GANs under different jamming signals, different JNRs, and different iteration counts, to verify its generalization. Figure 4 shows the process of the simultaneous convergence of the loss functions of the generator and the discriminator. Due to the mutual antagonism between the two networks, in the initial stage of training the oscillation of the generator loss is relatively obvious, reflecting that the learning of the time-frequency characteristics of the jamming samples by the generator of the GAN network is still unstable. From the overall trend, the loss function of the discriminator network shows a decreasing trend, while the loss function of the generator shows an increasing trend, indicating that the discrimination ability of the discriminator is improving. When the number of iterations exceeds 250, both the generator loss and the discriminator loss function tend to be stable, and the network can converge. In experiment 1, time-frequency feature learning was performed on the CSJ signal, and the signal was input into the GAN network for training. To illustrate that a GAN also has a good generation effect on different types of jamming signals, MTJ, FH, and LSJ were, respectively, selected as the target jamming signals. All the signals were normalized, and the JNR was set to 10 and then input into the GAN for adversarial training. As can be seen from Figure 5, for different types of jamming signals the network learned their time-frequency features. Among these, the jamming samples generated from the MTI and CSJ signals had the greatest similarity and almost overlapped with the original samples. The jamming samples generated from the LSJ signal possessed the time-frequency characteristics of the original jamming, but the start time slot of the attack was delayed by 50 time slots. The jamming samples generated from the FH signal had  
Figure 4: Generator and discriminator loss function variations during GAN training. relatively large differences from the original samples, but they conformed to the signal characteristics of Frequency-Hopping Jamming with fast carrier-frequency switching. The GAN could not only generate jamming samples with very high similarity to the original samples, meeting the subsequent needs of improving the decision-making accuracy and convergence speed of the DQN, but it could also generate jamming samples with relatively low similarity but similar jamming strategies. The added training data can bring diversity to the decision-making model and exert a better enhancement effect. Figure 6 presents the Pearson correlation coefficients between the samples generated under the JNR and iteration counts and the original jamming samples, demonstrating the generalization ability of the LACGAN. It can be seen from the figure that the Pearson correlation coefficient was basically positively correlated with the iteration count and JNR. As the iteration count or the JNR increased, the Pearson correlation coefficient also increased.