# Deep Learning-Based Optimization for Maritime Relay Networks
## 3 Relay Network Based on Deep Learning
It is evident that when comparing the predicted relay choices of the test set with corresponding optimal relay labels, the accuracy of the CNN-GRU-based model stabilizes at approximately 0.90 after 100 iterations, effectively meeting the expected performance. This result can be attributed to the model's training process, where the CNN-GRU-based relay selection model maps the feedback data from each link to the corresponding optimal label. Through iterative training, the model establishes connections and retains the learned mappings. During the prediction phase, the model accurately classifies and predicts the data based on these established mappings. Figure 4: Connectivity probability under different system parameters. To evaluate the effectiveness of the proposed scheme, a series of experiments were conducted to assess its performance. The comprehensive analysis and experimental results are as follows. The MSE performance of the proposed scheme under different Rayleigh fading channel parameters is depicted in Figure 7. In such channels, the received SNR follows an exponential distribution, where the distribution parameter \(\lambda\) was set to follow two different uniform distributions: \(U(1,2)\) and \(U(1,10)\). When \(\lambda\) follows the distribution \(U(1,2)\), the smaller fluctuation range of \(\lambda\) results in a limited set of samples during training. This restricted range introduces a bias into the generated samples, as they fail to represent the diversity of real-world conditions. Consequently, the model trained on these biased samples demonstrates reduced performance, as reflected in the higher MSE values. Conversely, when \(\lambda\) follows \(U(1,10)\), the broader fluctuation range provides a more diverse and representative sample set. This increased diversity improves the model's ability to generalize to different channel conditions, thereby reducing the MSE and enhancing performance. Figure 8 analyzes the impact of different input sequence lengths on system performance. Longer input sequences provide more historical information, enabling the model to better capture the temporal correlations of signals and thereby enhancing its performance. In contrast, shorter input sequences lead to information loss and insufficient model complexity, which negatively affect system performance. When the input length is set to 56, the system demonstrates the poorest performance due to an imbalance between model complexity and information richness. Figure 5: Curve of accuracy changes with increasing K.

Figure 6: Relay prediction accuracy curve across the time series. Maritime communication systems are significantly more affected by environmental conditions compared to terrestrial systems, with wave height being one of the critical influencing factors. Higher wave heights introduce greater signal fluctuation and instability due to harsher transmission conditions, leading to increased MSE. Conversely, lower wave heights provide a more stable environment, which improves signal quality and reduces MSE. To analyze this impact, we evaluated the MSE performance of the proposed scheme under two different average wave heights (\(\mu=1.5\) and \(\mu=4.5\)), as shown in Figure 9. The results indicate that as the wave height increases (i.e., the average wave height \(\mu\) grows), the MSE also increases accordingly. Compared to the impact of \(\lambda\) parameters on the MSE, wave height primarily affects the physical stability of signals, whereas Rayleigh fading introduces statistical variations in channel conditions. Both factors have a significant impact on system performance, highlighting the necessity of considering environmental variability in the design of robust maritime communication systems. Figure 8: MSE relative to input length in Rayleigh fading. Figure 7: MSE for different Rayleigh fading channel parameters. ## 5 Conclusions

In this paper, we explore the problem of relay-assisted maritime communication networks and propose a channel modeling method that closely represents real maritime environments.