## 5 Simulation Results  
In this section, we report the main results collected from the NR-CsiNet model and described in the previous sections. The assessment of the performance is carried out through simulation trials exploiting the NR link-level simulator presented in Section 3 by substituting the blocks for CSI feedback definition and reporting with our DL-based models. The transmitter precoding matrix is obtained by a single value decomposition (SVD) operation on the recovered channel matrix, which is generated from the compressed feedback sent back by the UE. This means selecting the eigenvector that corresponds to the higher eigenvalue as the precoding vector for the subsequent downlink transmission. Taking advantage of the spatial diversity given by the \(M\) receiving antennas, a maximal ratio combiner (MRC) is used at the receiving side. Table 1 reports the main parameters of interest concerning the simulator settings:  
More details on some of the main parameters are provided below:  
**Time slot**: Time duration of the transport block of data transmission, corresponding to 14 OFDM symbols. **Modulation and coding scheme (MCS)**: Configuration parameter of the system to determine the coding rate and the order and type of modulation to be adopted for the transmission. **Transport block size (TBS)**: Value corresponding to the number of bits transmitted in one slot. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**NR Simulator Parameters**} \\ \hline System bandwidth & 5 MHz \\ \hline Subcarrier spacing & 15 kHz \\ \hline Time slot & 1 ms \\ \hline Carrier frequency & 3.64 GHz \\ \hline iMCS & 21 \\ \hline (Modulation, coding rate) & (64-QAM, 0.694) \\ \hline TBS & 19,968 bit \\ \hline NFFT & 512 \\ \hline \(\widetilde{\mathcal{C}}\) & 300 \\ \hline Transmission layers & 1 \\ \hline Codewords & 1 \\ \hline CSI-RS Tx period & 4 slots \\ \hline CSI reporting type & Type I \\ \hline Channel model & CDL-B \\ \hline Delay spread model & Nominal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for NR-CsiNet training. * Smaller power of two greater than the number of sub-carriers used by the system to be used by FFT and IFFT operations. * Computed as the difference between the NFFT value and the number of sub-carriers composing the band guards. In our system \(\widetilde{C}=512-106-106\). * Parameter that determines the delay profile, affecting the scaling factor for the time of arrival of each cluster signal, leading to a shorter or longer delay spread. It should be noted that the adopted bandwidth is smaller than the one commonly expected for a 5G NR network. Behind this choice are considerations of computational complexity since a larger bandwidth would lead to a significant increase in simulation and training time. Table 2 shows the hyperparameters adopted for the training and testing process of our model. The DFT-compression factor \(K_{\text{DFT}}\), which should not be confused with the final compression factor \(K\) introduced in Section 4.5, is defined as:  
\[K_{\text{DFT}}=\frac{\widetilde{C}}{C} \tag{8}\]  
where \(\widetilde{C}\) and \(C\) are the same parameters defined in Section 4. The transmitting antenna system consists of a \(2\times 2\) planar array formed of dual-polarized antenna components, while the receiver is equipped with a single dual-polarized antenna; thus, we have \(N=8\) transmitting antennas and \(M=2\) receiving antennas. Since there are a relatively small number of antennas, it is obvious that this is not a massive MIMO scenario. This decision was forced by considerations solely related to computational complexity because adding more antennas will increase simulation times proportionately. Larger values of \(N\) and/or \(M\) should be envisioned for future research. In our system, the DFT-compression factor is fixed at \(K_{\text{DFT}}=3\). The training process of the neural network is performed for two different SNR levels: 10 dB and 20 dB.