## 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 addition, because the neural network structure is often regarded as a \"black box\", its internal working mechanism and decision-making process are difficult to understand and explain, leading to the poor interpretability of the data-driven approach. \begin{table}
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**Reference** & **Model** & **Input** & **Output** & **Compared with** & **Performance** & **Complexity** \\ \hline [PERSON] et al. [51] & DNN & Original channel & Estimated channel & SONIF algorithm & Has better NMSE performance and can reduce pilot overhead & \(O(2N_{\text{pl}}^{2}\rho)\) \\ \hline [PERSON] et al. [52] & DNN & Data subcarrier position index & Channel estimation of data subcarriers & LS algorithm & Has better NMSE and BER performance & \(\backslash\) \\ \hline [PERSON] et al. [53] & DNN & Received signal and pilot symbols & Estimated channel & COMP, MMSE, and burst LASSO algorithms & CoMF’d and the burst LASSO algorithms & CPU time = 0.338 ms \\ \hline [PERSON] et al. [54] & DNN & Received OFDM symbols and transmitted pilot symbols & Estimated channel & LS and MMSE algorithms & The SRE performance is close to the MMSE algorithm, one/80” buffer & Parameters = 257.186 K, CPU time = 2.975 ms \\ \hline [PERSON] et al. [55] & DAE-DNN & Noisy received signal & Estimated CSI & LS, OMF, MMSE algorithms, and DNN & Super-to-MSE methods in terms of noise environments & \(\backslash\) \\ \hline [PERSON] et al. [57] & ChannelNet (SSCNN + DNCNN) & Estimated channel at pilot positions & Estimated whole channel & Ideal MMSE and ideal MMSE algorithms & At NSR block 20.8 dB, the MSE  
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**Reference** & **Model** & **Input** & **Output** & **Compared with** & **Performance** & **Complexity** \\ \hline [PERSON] et al. [63] & Bi-RNN & Estimated channel at pilot symbols & Estimated whole channel & 2D-LMMSE algorithm, ChannelNet and Bi-LSTM & The link performance is superior to the & \(O(2\mathcal{O}_{\text{in}}+4\mathcal{G}^{2})\) \\  & & & & & ChannelNet, and Bi-LSTM & & \(\lambda\) slightly inferior to the & \(\lambda\) \\  & & & & & & \(22\mathcal{L}\)-Mass algorithm & \\ \hline [PERSON] et al. [64] & Bi-LSTM & Transmitted signal sequence & Prediction matrix of features & LS, MMSE algorithms, and and LSTM & With limited pilots, the SER performance & \(\backslash\) \\  & & & & input sequence & LS & MMSE algorithms, DNN, and ResNet & With limited pilots, the SER performance & \(\backslash\) \\  & & & & & and LSTM & is superior to all the & \\ \hline [PERSON] et al. [64] & GRU & Received signals & Transmitted signals & LS, MMSE algorithms, DNN, and ResNet & With limited pilots, the SER performance & \(\backslash\) \\  & & & & & and ResNet & is superior to other methods & \\ \hline [PERSON] et al. [64] & LSTM-GRU & Received signals & Estimated channel channel & CNN and CGAN & Has the best MSE performance & Computation time = 23.4 ms \\ \hline [PERSON] et al. [67] & SRGAN & Estimated channel at pilot positions & Estimated whole channel & LS, LMMSE algorithms, and ResNet & At low SNR, it provides the best MSE & FLOPs = 48.950 M, respectively, slightly inferior to the & parameters = 2.412 M, respectively & \(\lambda\) \\  & & & & & & \(\lambda\) [MMSE algorithm] & predict time = 3.464 ms & predict time = 3.464 ms \\ \hline [PERSON] et al. [68] & CGAN & Received signals and pilot sequences & Estimated channel & U-Net and CNN & Has the best MSE performance & Computation time = 25.88 ms \\ \hline [PERSON] et al. [69] & N2N-CGAN & Noisy pilot sequences & Estimated channel & LS, MMSE algorithms, and CGAN & In terms of MSE, it is superior to the LS & \\  & & & & & and CGAN & algorithm and CGAN and only slightly & \\  & & & & & & & \\ \hline [PERSON] et al. [70] & CAGAN (compete & & & & & \\ \(\lambda\)\(\pm\)\(\