## 1 Introduction  
The current advancement in telecommunication standards is represented by the fifth generation (5G) of mobile networks, which offer far more than the 4G/LTE networks. The demands of mobile network users, as well as those imposed by cutting-edge ideas like connected and autonomous cars, smart factories, or smart cities, are satisfied by 5G technology [1; 2; 3; 4]. In contrast to 4G/LTE networks, 5G has substantially greater data throughput (peak rate up to 20 Gbps), much lower latency (no more than 1 ms), much higher dependability (99.999%), and the capacity to serve a much higher number of terminals (device density up to one million per square kilometer) [3; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. On the other hand, electromagnetic waves are significantly impacted by the environment, despite the widespread deployment of 5G base stations. Therefore, in order to guarantee compliance with regulatory restrictions and to provide the best site selection for radio wave coverage, it is important to assess the electromagnetic radiation intensity of 5G base stations. Electromagnetic waves have a wide range of practical applications in daily life, including communication via cell phones, radio broadcasting, WiFi, cooking, vision, medical imaging, and cancer treatment (see Figure 1). There are now four primary methods to determine the electromagnetic radiation intensity of base stations: time-series analysis [17; 18], electromagnetic modeling [11; 12; 13; 14], measurement near base stations using a field-strength analyzer [4; 5; 6; 7; 8; 9; 10], and extrapolation methodology [15; 16; 17]. Other classes of methods include the following: indirect measurement methods, environmental measurement methods, wireless and remote sensing methods, biological and health impact measurements, mathematical and statistical approaches, and regulatory and compliance testing. While measurement is the standard technique for accurately estimating the quantity of electromagnetic radiation near 5G base stations, it has the drawback of being very time-consuming and expensive to monitor. In comparison to the method of moments (MOMs) and the finite difference time domain (FDTD), ray tracing and its acceleration algorithm offer a new approach for complicated electromagnetic environment prediction in a broad area with improved processing efficiency. The ray tracing algorithm's basic idea is to solve every path between base stations and field points and then use path information to calculate the electric field strength at each field point. The simulation software based on the ray tracing technique can quickly and reliably forecast the electromagnetic field (EMF) at various sites near base stations after importing transmitted antennas, nearby structures, and terrain features. However, because of three-dimensional beamforming,  
Figure 1: A typical 5G network architecture. massive multiple-input and multiple-output (MIMO), and other novel features of 5G base stations [18; 19; 20; 21; 22], the electromagnetic radiation intensity is greatly impacted by the locations, service modes, and quantity of 5G terminals in real-world scenarios. These factors are challenging to simulate in the simulation software. The following is an overview of the main features and benefits of massive MIMO and beamforming in 5G base stations:  
1. Better Coverage and Signal Quality: Massive MIMO extends coverage regions and enhances signal quality, even in highly congested urban locations, by utilizing beamforming and spatial multiplexing. This gives rise to improved user connectivity. Beamforming directs the transmitted energy toward the terminals instead of scattering it in different directions, as seen in Figure 2. In addition, by tracking the signals emanating from the terminals, the base station dynamically tracks their movement and modifies the ideal transmission direction. 2. Better User Capacity: The base station serves more users simultaneously and mitigates congestion using a large number of elements (up to 256). 3.