## 5 Simulation Results
Figures 6 and 7 highlight how the normalised mean square error (NMSE) of the channel reconstruction for the NR-CsiNet model does not depend on the training data SNR level, except for the lowest compression value \(K\) (\(K=1/120\)). The PDSCH throughput results for SNR training values of 10 dB and 20 dB are reported in Figures 8 and 9, respectively. The relevant SNR range is \([-5,23]\) dB. The results for the second evaluation metric, BLER1, i.e., BLER at the first transmission attempt, are reported in Figures 10 and 11 for the \([0,23]\) dB SNR range. The performance of the NR-CsiNet is compared with the PMI reporting technique, referred to here as follow PMI. This procedure requires channel measurements by the UE from CSI-RS, in addition to which a PMI is determined [37]. The PMI is an index that the UE sends back to the BS to suggest the preferable precoding vector corresponding to that index from a predefined precoding matrix. \begin{table}
\begin{tabular}{c c} \hline \hline  & **NR-CsiNet Parameters** \\ \hline Training set & 1000 sim of 100 slots \\ Validation set & 100 sim of 100 slots \\ Testing set & 100 sim of 100 slots \\ Learning rate & 0.001 \\ Loss function & MSE \\ Optimizer & Adam \\ Epochs & 300 \\ RefineNet units & 2 \\ \(K_{\text{DFT}}\) & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NR-CsiNet model parameters [32]. Figure 8: Throughput statistics for NR-CsiNet model trained at SNR = 10 dB. Figure 6: NMSE statistics for NR-CsiNet model trained at SNR = 10 dB. Figure 7: NMSE statistics for NR-CsiNet model trained at SNR = 20 dB. Figure 11: BLER1 statistics for NR-CsiNet model trained at SNR = 20 dB [32]. Figure 10: BLER1 statistics for NR-CsiNet model trained at SNR = 10 dB. Figure 9: Throughput statistics for NR-CsiNet model trained at SNR = 20 dB [32]. It is possible to infer from the graphs that, even when dealing with noisy channel estimates rather than ideal channel matrices, our DL-based technique performs better than the follow PMI algorithm in \(8\times 2\) cases. The plots specifically illustrate that over 20 dB of SNR, both the NR-CsiNet and follow PMI throughput curves achieve the transport block size (TBS) maximum limit; however at lower SNR, all the variations of our DL solution perform better. It is important to note that, in all of our experiments, we used the same channel model configuration. The simulation results demonstrated that, despite being intended for multipath contexts, this type of channel model typically presents a dominant path, which implicitly defines a preferential direction for signal transmission. It is obvious that the follow PMI is expected to function well when taking into account such a channel model. In fact, among a range of potential beams, the PMI index merely determines which one is most precisely pointed to the receiver. Our hypothesis is that our DL-based technique has the best chance of outperforming the follow PMI algorithm in \"richer\" channel realizations when the multipath is not dominated by a single LOS cluster. The complexity of DL-based solutions is one of the main shortcomings in their implementation at the user equipment side, characterized by limited power and hardware resources. In Table 3, we report the number of parameters and FLOPs required for the NR-CsiNet encoder, which are greatly dependent on the choice of the factor \(K\). For the CNN layer, the number of learnable parameters are computed as \(par_{CNN}=\#\ filters\cdot(filter\ width\cdot filter\ height\cdot input\ feature\ maps+1)\) while the FLOPs are \(FLOP_{\text{CNN}}=2\cdot\#\ filters\cdot filter\ width\cdot filter\ height\cdot Output\ size\); for the FC layer, the parameters are \(par_{FC}=(Input\ size+1)\cdot Output\ size\), while the FLOPs are \(FLOP_{\text{FCL}}=2\cdot Input\ size\cdot Output\ size\). The FLOP amount is of the same order of magnitude for both the CNN and the FC, while the FC requires a significantly higher number of parameters since it needs a weight for each input-output node link (plus the bias).