## 3 Deep-Learning-Based Channel Estimation Methods
### Data-Driven Deep-Learning-Based Channel Estimation Methods
#### 3.3.4 Application of GAN in Data-Driven Channel Estimation  
In channel estimation, GAN has the ability to serve by generating additional data to augment the required training dataset, which is particularly valuable in scenarios without sufficient information about the actual CSI. Moreover, GAN can adaptively improve its performance during training, thereby achieving more accurate estimation results through the adversarial process between the generator and the discriminator. Motivated by ReEsNet in [58], [PERSON] et al. [67] developed a super-resolution GAN (SRGAN) for channel estimation in 2021. However, the estimation results of ReEsNet have lost high-frequency details and failed to match the desired fidelity at higher resolutions. In the SRGAN scheme, the channel generated by the generator is closer to the actual channel distribution. Meanwhile, the discriminator recovers more details, significantly enhancing the estimation accuracy. Through experimental comparison, the SRGAN's MSE performance surpasses both the LS algorithm and the ReEsNet, and it approaches the MMSE algorithm in high SNR. For massive MIMO systems, [PERSON] et al. [68] investigated a CGAN channel estimator in the same year. The proposed CGAN learns how to map from quantized observations to the actual channel and learns a suitable adaptive loss function for network training, resulting in a highly robust model and a more realistic estimated channel. Even when the model is tested on a different channel (i.e., a millimeter wave channel), its performance remains almost unchanged, demonstrating its exceptional generalization capability. In 2023, [PERSON] et al. [69] also employed CGAN, but their focus was obtaining accurate CSI for MIMO-OFDM systems in high-speed railway scenarios. Their scheme is executed in two steps and regards the pilot signal as a 2D image. In the denoising step that employs the U-Net framework, the received noisy pilot image is denoised using the Noise2 Noise (N2N) algorithm. In the estimation step, the CGAN completes the channel estimation by learning the features of the denoised pilot image. Through experimental verification, the N2N-CGAN has high estimation accuracy in high-speed mobile scenarios, exhibits strong robustness in noisy environments, and has low computational complexity. In the pilot-based channel estimation scheme, the estimation efficacy highly depends on the pilot insertion pattern. To improve the estimation accuracy while reducing the pilot overhead, [PERSON] et al. [70] proposed a network model called CAGAN as a channel estimation scheme for joint pilot design. The CAGAN comprises a CGAN and a concrete autoencoder (concrete AE). First, the concrete AE is used to search for the most informative location in the time-frequency grid and insert pilots here, thereby optimizing the pilot design. Next, the optimized pilots are fed into the CGAN for channel estimation. Experimental results show that the CGAN can exhibit outstanding estimation performance with a limited number of pilots. In order to verify the generalization ability of noise, the CAGANs trained with an SNR of 15 dB and trained at different SNRs are compared in a 0-30 dB SNR range. The result demonstrates that, when the SNR is above 9 dB, the performance of these two models is comparable. Table 3 summarizes the reviewed data-driven deep-learning-based channel estimation methods and their performance. This type of method exhibits high performance when dealing with high-dimensional, complex data, and it can also show self-learning capabilities in capturing channel characteristics. However, these methods also have glaring disadvantages. They are highly reliant upon an extensive quantity of training data; if the training data are insufficient or not well regularized, this could lead to overfitting, resulting in a decline in generalization ability on new data.