## 3 Numerical Results
### System-Level Simulations
Considering an \(F=828\) and 1C the version of the algorithm based on SPS can reach a throughput of 85.79 Gbps whereas the one based on DPS can reach a throughput of 99.99 Gbps. When we have 3C, both versions of the algorithm can obtain a significant improvement over the values obtained for 1C. The first one can reach a throughput of around 127.3 Gbps and the latter one reaches 132 Gbps. In fact, the version based on a DPS architecture with 3C is the one that can closely approach the digital precoding curve. Figure 8: Throughput (Gbps) vs. number of users for different number of subcarriers, RF chains and cluster sizes, considering the proposed precoder with \(N_{\text{tr}}=256\), \(N_{\text{rx}}=4\) and \(N_{\text{s}}=2\) (only NLOS component). After previously analyzing two different solutions based on phase shifters, we now evaluate the performance of the system in terms of throughput and coverage with more realistic phase shifters, i.e., phase shifters with quantization. Figure 10 is based on the link-level simulation of Figure 6 presented in the previous subsection. From this figure, we observe that, as expected, by increasing the number of quantization (QPS curves) it is possible to approach the performance of the ideal unquantized version of the algorithm (SPS curve). Furthermore, it can also be seen that by increasing the cluster size, the throughput can almost double when the number of subcarriers is large. Moreover, we observe that by increasing the cluster size from 1C to 3C that the hybrid precoders curves get closer to the digital precoder curve. A lower throughput loss, due to the quantization effect, is also observed. Following the conclusions about the previous figure concerning the impact of quantized phases shifter in the hybrid architectures, we decided to study the system performance using the same hybrid architecture in the InD-MO and the InD-OO scenarios. The throughput per user results of Figure 11 and the average coverage results of Figure 12, were simulated considering a precoder based on QPS with \(N_{b}=3\) and 4 bits. It can be observed in Figure 11 for the InD-MO scenario, that by increasing the cluster size the throughput will improve and if we consider the effect of using more quantization bits the difference becomes more notorious. When the cluster size increases from one to three, we can observe at 100 mW that the throughput doubles in both cases of quantization. If we consider the InD-OO scenario, the obtained gains present a similar behavior as the power increases but the throughput we can reach will be significantly lower. The difference between these two scenarios lies in the relative weight of the LOS and NLOS components, as the attenuation losses associated with them are the same in both scenarios. In the InD-MO scenario, the \(P_{LOS}\) is 1 up to 1.2 m and decreases to 0.368 at 6.5 m.  
Figure 9: Throughput (Gbps) vs. number of subcarriers for different versions of the algorithm (SPS and DPS) and cluster sizes, considering the proposed precoder with \(N_{tx}=256\), \(N_{rx}=4\), \(N_{RF}^{tx}=14\), \(N_{s}=2\) and \(N_{u}=180\) (with LOS component). Figure 11: Throughput (Mbps) versus mean value of power required by each user (mW), considering the proposed precoder with \(N_{tx}=256\), \(N_{rx}=4\), \(N_{RF}^{tx}=14\), \(N_{s}=2\), \(F=828\) and the quantization effect (with LOS component). Figure 10: Throughput (Gbps) vs. number of subcarriers for different versions of the algorithm (with and without quantization) and cluster sizes, considering the proposed precoder with \(N_{tx}=256\), \(N_{rx}=4\), \(N_{RF}^{tx}=14\), \(N_{s}=2\) and \(N_{u}=180\) (only NLOS component). In the InD-OO scenario, the \(P_{LOS}\) is 1 up until 5.0 m and decreases to 0.538 at 49.0 m. Knowing that the \(P_{NLOS}\) is equal to \(1-P_{LOS}\), we realize that in the InD-MO scenario the weight of the NLOS component will be greater than that of the LOS component.