## 5 Simulation Results and Analysis
The network obtains the best  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**Attention-CoilNet**} & \multicolumn{2}{c}{**LSTM-Attention CoilNet**} & \multicolumn{2}{c}{**SALDR**} & \multicolumn{2}{c}{**CsiFormer**} & \multicolumn{2}{c}{**TransNet**} & \multicolumn{2}{c}{**TransNet**} \\ \hline
**\#** & **in** & **out** & **FLOPs** & **in** & **out** & **FLOPs** & **in** & **out** & **FLOPs** & **in** & **out** & **FLOPs** & **in** & **out** & **FLOPs** & **in** & **out** & **FLOPs** \\ \hline
1/4 & \(-\)20.29 & \(-\)10.43 & 24.72 M & \(-\)22.00 & \(-\)10.20 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(-\)32.28 &  
results when \(\eta=1/4,1/32\) with \(n_{\text{head}}=4\). The gaps between the results for \(n_{\text{head}}=4\) and the best results at the other three compression ratios are merely 0.36 dB, 0.29 dB, and 0.55 dB, respectively. In terms of overall performance, this gap is too minor to be taken into account. In general, the optimal number of attention heads is a balance between computational cost, number of parameters, and performance on accuracy. The model will reach a balance at a certain number of attention heads when the compression ratio is different. All we can do is select the number of attention heads that performs well at all compression ratios. Therefore, \(n_{\text{head}}\) selected in our network is 4 for both considerations of stability and recovery accuracy. In Figure 4, we compare the NMSE of CRNet and TransNet+ with the cosine training scheme and TransNet+ with a constant learning rate. After 100 epochs, our model outperforms CNN-based CRNet by a large margin. The results of the first 400 epochs indicate that TransNet+ with the cosine training scheme converges faster. Meanwhile, the results of the remaining epochs demonstrate that TransNet+ has superior recovery accuracy. This is because the warm-up cosine training scheme has a high learning rate at the early stage, which makes the model learn features rapidly and accelerate the training speed, and a lower learning rate at the later stage, allowing the model to learn high-resolution features to obtain better feedback quality. Simultaneously, the higher dimensional feature representation also contributes to the improvements in recovery accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\eta\) & **FLOPs** & \(n_{\text{head}}=\mathbf{1}\) & \(n_{\text{head}}=\mathbf{2}\) & \(n_{\text{head}}=\mathbf{4}\) & \(n_{\text{head}}=\mathbf{8}\) \\ \hline \(1/4\) & \(35.652\) M & \(*\) & \(-29.42\) & \(\mathbf{-30.58}\) & \(-7.58\) \\ \(1/8\) & \(34.603\) M & \(-22.28\) & \(\mathbf{-22.83}\) & \(-22.47\) & \(-22.75\) \\ \(1/16\) & \(34.079\) M & \(-15.73\) & \(-15.90\) & \(-15.80\) & \(\mathbf{-16.09}\) \\ \(1/32\) & \(33.817\) M & \(-11.55\) & \(-11.06\) & \(\mathbf{-11.81}\) & \(-10.98\) \\ \(1/64\) & \(33.686\) M & \(\mathbf{-7.46}\) & \(-5.91\) & \(-6.91\) & \(-6.65\) \\ \hline \hline \end{tabular} \({}^{*}\) means the data does not converge during training.  
\end{table} 
Table 2: NMSE(dB) for different \(n_{\text{head}}\) of the multi-head attention layer. Figure 4: The trends of NMSE during testing at \(\eta=1/4\) in indoor scenarios.