## 3 Method: Massive MIMO Network Design
### Optimization Algorithm  
In this paper, we proceed with the simulations of the optimal massive MIMO 5G networks to investigate the scenarios described above. We apply a capacity-based network deployment tool proposed in [19], in which the optimization algorithm in Figure 3 is implemented. The optimization algorithm of the tool is based on the BS-user association principle and optimizes the initial set of the base stations deployed in the area of interest while meeting the following optimization constraints: (i) the obtained network must have the least possible power consumption, (ii) the DL and UL EMF exposure resulting from the obtained 5G networks must be minimal, (iii) the network must respond to the instantaneous bit rates (voice and data) requested by the users and (iv) must provide a coverage for at least 95% of the users. This algorithm is designed in such a way that the decision to associate a user with the base station is related to the value of the fitness function. The algorithm consists of two stages. In the first stage (Figure 3, step 2), a list of possible candidates BSs (active or not) to connect to the users is \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **Symbols** & **Values** & **Units** \\ \hline Reference Specific & \(SAR_{ref}^{DL}\) & 0.0048 & \(\frac{\text{W}}{\text{K}_{\text{g}}}\) per \(\frac{\text{W}}{\text{m}^{2}}\) \\ \hline Reference Specific & \(SAR_{ref}^{UL}\) & 0.0052 & \(\frac{\text{W}}{\text{K}_{\text{g}}}\) per W \\ \hline TDD duty cycle in DL & \(DC_{TDD}^{DL}\) & 0.75 & [-] \\ \hline TDD duty cycle in UL & \(DC_{TDD}^{UL}\) & 0.25 & [-] \\ \hline Spatial duty cycle in DL & \(DC_{SPA}\) & 0.15 & [-] \\ \hline Time duration in DL & \(T_{d}^{DL}\) & 3600 & s \\ \hline Time duration in UL & \(T_{d}^{UL}\) & 35 & s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters considered for the evaluation of the exposure. Figure 3: Optimization algorithm implemented in the capacity-based network deployment, designing optimized networks towards power consumption, downlink (DL) and uplink (UL) exposure. constructed based on the path loss (PL) between the user and each BS. For a BS to be included in the list of candidates BSs, two conditions must be verified:  
* For each user, the algorithm evaluates the PL between the user and all the BSs. This PL should be lower than the maximum allowable path loss (MAPL);
* The capacity \(C\) of the BS should be high enough to support the high bit rate \(C_{u}\) demanded by the user \(u\) (\(\sum_{u}C_{u}\leq C\)). In the second stage (Figure 3, step 3), the algorithm estimates the fitness function for each of the BS inserted in the list of possible candidate BSs. The BS with the highest fitness value is associated with the user and enabled in case the BS was inactive. If there are already enabled (active) BSs within the network, the algorithm checks the possibility to transfer the users already associated with that BS to this newly active BS (Figure 3, step 5). This transfer requires the new enabled BS to present the highest fitness value. If no BS can be enabled, then the user cannot be covered. These operations are repeated for all the users. This results into an optimal massive MIMO 5G network with the highest fitness value. This latter shows a better trade-off between the low-power consumption, the low DL and UL EMF exposure and the users' coverage requirements. The computation of this algorithm requires the similar input files as in [19]:  
* A traffic file containing the maximum number of simultaneous active users and their locations,
* The link budget parameter files (Table 2),
* The 3-dimension (3D) shapes of the environment of study,
* The power consumption file of the individual BS components. To ensure the massive MIMO requirements in terms of higher data rates imposed by the 5G applications are met, we consider the 3 GPP LTE Rel. 8 path loss model \(PL(d)\) (Equation (24)) and the power consumption model proposed in [19; 34].