## 1 Introduction
Reduction of Interference: By spatially separating signals, interference is mitigated, consequently improving the signal-to-noise ratio and overall network performance. 4. Energy Efficiency: By concentrating transmission power where it is required instead of dispersing evenly in all directions, massive MIMO is energy-efficient despite its higher antenna requirement. The extrapolation technique uses measurements made over a short period to determine the maximum level of the electromagnetic field generated by a base station [15]. The scheduler must, however, continually allot all available resources to one user for at least six minutes, leaving all other users in standby mode, in order to accurately estimate the maximum level. This leads to an unfeasible loss of data connection of at least six minutes. In order to overcome this restriction, the extrapolation method approximates the maximum radiation field intensity by using the synchronization signal block (SB) in conjunction with specific extrapolation factors. Even with this improvement, the method still requires laborious computation processes and has high testing costs. Predicting the time-varying electromagnetic radiation in the vicinity of base stations is another common use for time-series analysis. For instance, [PERSON] et al. constructed a  
Figure 2: Diagrammatic representation of the beam that the 5G base station emits. data set based on 36-month recorded EMF and forecasted the upcoming 12-month EMF using traditional models and artificial neural network (ANN) models, respectively [18]. [PERSON] et al. developed a time-series analysis model based on data collected over a period of seven days in order to forecast the electric field on weekdays and weekends, as well as the peak exposure time of the day [19]. These models are able to accurately predict the future trend of radiation levels close to base stations based on historical data. In situ RF exposure measurement surveys have been conducted for several 5G commercial networks globally [2; 3; 4; 5; 6; 7]. These surveys not only measure the actual exposure levels, which include the power density (or field strength) from the base station (BS) under current operating conditions (such as traffic load, number of connected devices, their distribution, and scheduled beams), but also assess the potential maximum exposure levels. For a specific measurement location or point of interest (POI), the maximum exposure corresponds to the power density when the BS is transmitting at full power, which represents a scenario where the BS operates at 100% capacity. To estimate this, appropriate extrapolation techniques for 5G BSs have been discussed in the literature [7; 9; 10; 11; 12] and incorporated into relevant standards [13]. These methodologies are applicable to both frequencies below 6 GHz and higher frequency bands [14]. However, as emphasized in [15], 5G networks operating at millimeter-wave (mmW) frequencies pose unique challenges that must be taken into account when conducting EMF exposure measurements. The examples are as follows:  
1. For the use of directional antennas or, alternatively, single-axis omnidirectional probes when conducting EMF assessments with directional antennas, careful positioning is essential to capture exposure contributions from all relevant directions. 2. The shorter wavelength enables the use of a greater number of antenna elements. As a result, the antenna beamwidth may be narrower compared to that of typical massive MIMO (m-MIMO) base stations operating below 6 GHz. However, machine learning regression algorithms are crucial for forecasting continuous numerical values based on available data [23; 24; 25; 26; 27]. The random forest model is highly justified for prediction tasks due to its robustness, ability to handle complex relationships, resistance to overfitting, and flexibility in dealing with various types of data.