# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 1 Introduction  
In the field of communication, channel estimation is a key link to ensure the reliable transmission of signals. However, traditional channel estimation methods have certain limitations when facing complex environments or scenarios with channel sparsity. Although the application of deep learning has brought about some progress, there are still flaws in the network structure and data form. Exploring more accurate and effective channel estimation methods has become an important research direction at present. In today's technologicalfield, deep learning has been widely applied, yet adversarial attacks have become a major obstacle to its development. The core of adversarial attacks lies in inputting deceptive data into machine learning models, resulting in misclassification of the models. Deep learning adversarial attacks originated in the field of images [1] and then gradually expanded to multiple other fields. During the implementation of adversarial attacks, attackers will perturb the original samples based on their in-depth understanding of the model, such as neural network architecture, learning parameters, loss functions, etc., thereby maximizing the loss function of the classifier and ultimately leading to classification errors. With the advancement of research, adversarial attack algorithms continue to evolve. On the one hand, attackers begin to focus on frequency-domain information because different frequency components have a non-negligible impact on the judgment of the model. By using techniques such as frequency-domain adversarial generative networks [2], spectral attacks [3], and frequency-domain adversarial transport [4], the model can be ingeniously deceived at the frequency-domain level, thereby improving the success rate of attacks. Frequency-domain analysis also helps to deeply understand the model's response to different frequency components, providing strong guidance for the generation of adversarial samples. This is of great significance for improving the defense mechanism and enhancing the robustness of the model. In the generation of adversarial samples based on frequency-domain transformation considering channel information, if channel estimation is lacking, the generated adversarial samples are difficult to adapt to the characteristics of the actual communication channel. Due to the failure to consider factors such as multipath propagation, fading, and frequency offset in the channel, in the wireless communication scenario, the adversarial samples may undergo severe distortion after being transmitted through the channel, which greatly reduces the success rate of the attack and makes it impossible to effectively evaluate the security of the model in the real channel environment. Channel estimation can accurately describe the channel characteristics, providing a key basis for the generation of adversarial samples in frequency-domain transformation. This enables the generated samples to optimize the perturbation addition strategy based on the channel conditions, ensuring that they remain deceptive after being transmitted through the channel. Therefore, it is extremely urgent to explore new channel estimation methods, improve the estimation accuracy, and verify their importance in the generation of adversarial samples. This paper proposes an innovative channel estimation method for these problems and verifies it with the aid of adversarial sample generation based on frequency-domain transformation. The specific contributions are as follows:  
1. A channel estimation method based on deep learning is proposed. This method fully exploits the potential of deep learning in data processing and feature extraction and is committed to achieving high-precision estimation of sparse channels in OFDM systems. 2. Based on the above channel estimation methods, a channel-aware adversarial sample generation method based on frequency-domain transformation is further proposed to verify the importance of SDRNet channel estimation and significantly improve the effect of adversarial sample generation. 3.