# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Accurate Channel Estimation for mmWave Systems
#### 4.2.2 Row-Compressed Two-Stage LAMP (RCTS-LAMP) for Channel Estimation
To simultaneously optimize both of the adjustable parameters \(W_{1}\) and \(W_{2}\) of the LAMP networks, we consider the combined NMSE loss for \(H_{e}\) and \(H_{e}\), which has the following definition. \(H_{d}^{e(u)}\) and \(H_{C}^{e(u)}\) represent the direct calculation, and the parameters \(W_{1}\) and \(W_{2}\) enhance the ability of the two LAMP networks to recover \(H_{e}\) despite the impact of estimation errors caused by estimating \(\mathrm{hd}\) with the first LAMP network. The primary goal of the three-stage training approach is to enhance the versatility of the initial LAMP network by utilizing a common adjustable parameter \(W_{1}=\left\{Q_{l,1},X_{l,1},\alpha_{i,1},\right\}_{l=1}^{L}\); it can also be effectively implemented with the extra adjustable parameter \(W_{d}\) for the first LAMP network in direct channel estimation methods. This means that we should take into account the adjustable parameter \(W_{d}\) in the direct channel estimation for the training procedures in stages 2 and 3. During stage 2, \(W_{d}\) is set using the parameter \(W_{1}\) obtained in stage 1 and is improved through transfer learning. During stage 3, \(W_{d}\) is optimized together with \(W_{1}\) and \(W_{2}\), considering the joint NMSE loss \(L_{IO}\) in Equation (22). Including the modifiable parameter \(W_{d}\) could enhance the representational capacity of the initial LAMP network and could potentially enhance the precision of both direct and cascaded predictions. Extra channel memory overhead may impede deployment in real-world systems. Table 3 displays a study of various weight-sharing approaches that target improving the balance between estimation accuracy and memory usage. Both ends of the spectrum are exemplified by the strategies of complete sharing and complete non-sharing. The original RCTs-LAMP network can facilitate direct and cascaded channel estimations by implementing the all-shared technique. Additionally, a separate LAMP network will be created using the non-shared method for direct channel estimation. We also take into account the idea of splitting \(\alpha\), noting that the shrinkage parameters \(\alpha_{i}\) and the linear transformation matrices (\(Q_{l}\) and \(X_{l}\)) play different roles in the LAMP network. Although utilizing the individual \(\alpha\) may result in minimal memory usage, it can provide customized shrinkage parameters by considering the power of each signal when reconstructing \(H_{row}\) and \(\widetilde{H_{c}}\). Nonetheless, by utilizing the identical matrix \(A_{1}\) to measure both \(\widetilde{H_{c}}\) and \(H_{row}\), along with their analogous signal patterns, the recovery of \(H_{row}\) and \(\widetilde{H_{c}}\) can be achieved using the same transform matrices (\(Q_{l}\) and \(X_{l}\)), resulting in no decrease in performance. Therefore, the approach of the distinct \(\alpha\) is better for the retrieval of \(H_{row}\) and \(\widetilde{H_{c}}\), as it can enhance the estimation results without significant memory usage.