## 3 Numerical Results
### System-Level Simulations
Since the attenuation losses of NLOS are much higher than those of the LOS component, the received power of the Aps decreases rapidly with distance. Therefore, in the case of the InD-OO scenario, the weight of the LOS component is greater than the one of the NLOS component, so the transmitted power of the Aps decreases very slowly with distance generating a strong inter-site interference, which affects the maximum throughput and coverage that can be reached. Moreover, the channel can influence the results since with a stronger LOS component the multipath (NLOS component) becomes weaker, which worsens the spatial multiplexing and the spatial diversity effects. Figure 12 exhibits similar behavior when compared to the previous figure, since the greater the cluster size and the number of quantization bits, the greater the achieved coverage will be. With 3 and 4 bits of quantization at 100 mW and when the cluster size increases from one to three, we obtain a significant improvement which almost doubles the initial coverage. If we consider the InD-OO scenario, the obtained gains in terms of coverage present a similar behavior as the power increases but we can no longer obtain more than 73% of coverage (\(N_{b}\) = 4 bits and 3C). In order to understand the influence of the variation of the transmitted power (\(P_{t}\)) of the Aps on the system-level simulation results, we considered two InD scenarios with the proposed precoder based on a DPS architecture with \(N_{tx}\) = 256, \(N_{rx}\) = 4, \(N_{RF}^{tx}\) = 14, \(N_{s}\) = 2 and \(F\) = 828. In Table 3, we present the results of average throughput per user for both InD-OO and InD-MO scenarios when we vary the transmitted power (\(P_{t}\)) of each AP. We considered that \(P_{t}\in\{10,100,1000\}\) mW. As expected, based on Figures 11 and 12, the performance of scenario MO is higher than OO due to less inter-cell interference. For the same reason cluster 3C presents higher performance than cluster 1C. We can check that independently of the scenario and cluster size there is an optimum value of \(P_{t}\), which  
Figure 12: Coverage (%) 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). is 100 mW. The highest values of throughput occur for 100 mW. This value of \(P_{t}\) was considered in all system-level simulations of this paper. It corresponds to the best trade-off between transmitted power and inter-cell interference. For cluster size one (1C) and InD-OO, the performance with 10 mW tends to be higher than 1000 mW due to the high inter-interference of this cluster. However, for cluster size (3C) the opposite occurs because of the lower inter-interference of this cluster. To reduce the large performance loss due to the adoption of a simple AoSA architecture, we can allow the dynamic connection of more subarrays to each RF chain by adopting a DaoSA structure and studying the impact of the use of SPS and DPS architectures from a system-level perspective. The goal of these structures is to try to reach a compromise between fully connected structures, which are more complex to implement (especially with a massive number of antennas), and AoSAs (\(L_{max}=1\)), which are lighter but typically suffer a large performance loss. It is possible to conclude, through Figures 13 and 14, that the performance of the system can be improved as the \(L_{max}\) increases. The use of DPS instead of using SPS architectures can provide higher throughputs but the difference between the gains tend to reduce when the cluster size increases. When we have 1C the gains of the DPS over the SPS can surpass 50% and with 3C we obtain gains around 20%. In general, by combining the increase in \(L_{\text{max}}\) with the adoption of DPS and the cluster size, it is possible to improve the results, but the gains become less pronounced for \(L_{\text{max}}>1\).