Influence of Autoencoder-Based Data Augmentation on Deep Learning-Based Wireless Communication
时间: 2024-05-22 16:16:43 浏览: 20
Autoencoder-based data augmentation can have a significant influence on deep learning-based wireless communication systems. By generating additional training data through data augmentation, the performance of deep learning models can be greatly improved. This is particularly important in wireless communication systems, where the availability of large amounts of labeled data is often limited.
Autoencoder-based data augmentation techniques can be used to generate synthetic data that is similar to the real-world data. This can help to address the problem of overfitting, where the deep learning model becomes too specialized to the training data and performs poorly on new, unseen data. By increasing the diversity of the training data, the deep learning model is better able to generalize to new data and improve its performance.
Furthermore, autoencoder-based data augmentation can also be used to improve the robustness of deep learning models to channel variations and noise. By generating synthetic data that simulates different channel conditions and noise levels, the deep learning model can be trained to be more resilient to these factors. This can result in improved performance in real-world wireless communication scenarios, where channel conditions and noise levels can vary widely.
In conclusion, autoencoder-based data augmentation can have a significant influence on deep learning-based wireless communication systems by improving the performance and robustness of deep learning models.