## 4 \(N_{g}\) Determination and Related System Structure
In this situation, \(N_{g}\) can be determined based on the minimum required SE in a given circumstances. In particular, for the energy efficient sensor networks, if the minimum required SE is satisfied, the system can operates efficiently with high energy efficiency and a moderate data rate. Also, if determination of \(N_{g}\) causes lower SE than the requirement, it could be problematic to the proper operation of sensor networks. It is particularly important to maintain the required data rate for industrial IoT and/or sensor networks. If \(N_{g}\) can be derived as a closed-form equation, it can be very useful to reduce the computational complexity and delay of the system. The inverses of Equations (28) and(29) can provide \(N_{g}\) with given required SE, however the exact closed-form inverse equations of Equations (28) and(29) are impossible, because \(N_{g}\) terms exist both inside and outside of log term. Instead, by keeping the \(\frac{\eta_{CRS}+(N_{t}+K\cdot\Phi)}{\eta_{R_{bat}}N_{S}}\) as 0.5, the minimum  
First, the central management unit can decide the power consumption and/or power efficiency of the system. Then, BS measures current power efficiency. BS also has various operation parameters, such as \(N_{t}\), \(K\), \(B\), and \(\rho_{t}\) and so on. Based on the power efficiency and operation parameters, the minimum data rate that must be satisfied for the system can be derived. If the system is designed for for Industrial internet, there would be a threshold for data rate, and the system should set the minimum data rate higher than the threshold. Then, from the minimum data rate and other parameters that are necessary, \(N_{g}\) can be determined. The BS can receive feedback from UEs to check if they need a higher data rate depending on the situation. Wireless channel is not stable, so there could be several situations that can arise. Then, the procedures we mentioned can be repeated for a given number of iterations. There could be an interval for the determination of \(N_{g}\). If the interval is small, then the performance would be better, but complexity could be a problem or vice versa. Example of operation flow chart is presented in Figure 4. First, BS can get system parameters, then it gets required power efficiency. Based on system parameters and power efficiency, the required minimum data rate can be derived. The \(N_{g}\) can be derived using the proposed closed-form equation. The RS can be designed and used based on \(N_{g}\). Then, communications between massive MIMO and distributed IoT devices would be conducted. If power consumption is higher than the threshold, we can reduce power consumption by any kind of parameter adjustment. Typically, TX power consumes a lot of power, thus it can be reduced to increase power efficiency. However, TX power reduction accompanies data rate reduction. If the data rate is higher than threshold even though TX power is reduced, then system is in good shape and the procedure can be finished, but if data rate is lower than the threshold due to the TX power reduction, then in a given number of iterations, we should reduce required minimum data rate to satisfy power efficiency. The determination of thresholds for the number of iterations, power consumptions, data rate are the system designer's choice. For the better understand and implementation, we provide the detailed algorithm as pseudo-code type in Algorithm 1. The required number of iterations for the system can be changed based on system parameters. There are several parameters including the threshold for the number of iterations, thus system designer can decide it between performance and delay/complexity trade-off. Figure 3: Example of system block diagram for proposed scheme.