## 2 Related Work
(2018) proposed an algorithm for improving the handover decisions between two base stations (BSs), eliminating UE assistance and HO reduction. They used ML to predict the SNR of transmissions between two 5G cellular base stations. [PERSON] et al. [PERSON] et al. (2018) proposed an LSTM-based system for predicting multi-directional link quality in mm-wave systems in wireless systems. It simulated multi-cell link and vehicular blockers at 28 GHz and 140 GHz and beamed prediction in an indoor setting at 60 GHz. [PERSON] et al. [PERSON] et al. (2018) outlined the application and features of high-altitude platforms (HAP) to deliver future broadband wireless communications. The term building entry loss corresponds to the definition made at ITU-R. Ref. [PERSON] et al. (2018) defined it as the excess loss due to building walls and other building features. Building entry loss depends on the building type, construction, and electrical parameters of the material used. Measurements of penetration loss of various building materials at 1-8  GHz appear in [PERSON] et al. (2018). The authors of [PERSON] et al. (2018) presented electrical parameters of materials of S-band frequencies. Many other aspects influence the building entry loss, such as the receiver's position inside the building, the transmitter, and so forth, making it difficult to accurately estimate the level of received power inside a building. A decrease of the entry losses into building with floor height can often be observed [PERSON] et al. (2018); [PERSON] et al. (2018). Entry loss measurements for 2 GHz were reported in [PERSON] et al. (2018); [PERSON] et al. (2018). Measurements of the spread of the received signal within buildings were reported in [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). In Ref. [PERSON] et al. (2018), the authors focused on the overall building entry loss and building entry loss as a function of elevation and entry angle and the results of time-delayspread. The authors of Refs. [30; 31] presented a new model of the path-loss parameters. [PERSON] et al. [32] described a simple way to measure electric wave attenuation within an indoor scenario, demonstrating attenuation level differences for different wall materials and textures. [PERSON] et al. [33] proposed a framework for modeling satellite-to-ground signal attenuation in urban environments. They captured the shadowing using measurements collected from a global navigation satellite system (GNSS). The mentioned methods were based on various data analysis methods to identify the contributing parameters to signal loss. This approach requires proof of the truth of the found parameters. Our proposed system is based on accumulated data processed by proven ML methodologies, generating improved and accurate outcomes. More recent research is found in [PERSON] et al. [34]. In the IoT context, they explored the satellite multicast communication integrated with rate-splitting, multiple-access operating in the same frequency band. The authors of Ref. [35] proposed safe and efficient energy transmission for satellite-ground networks, and Ref. [36] explored power allocation for non-orthogonal, multiple-access satellite-ground networks. In summary, we reviewed rain attenuation and related prediction models for section A. Some focus on a specific range of frequencies, particular world regions, or wireless systems but do not provide a comprehensive approach covering a broader frequency range or models independent of a specific area. For section B, we reviewed several proposals dependent on particular setups. This work proposes a general prediction model that can be adjusted to comply with any region and a broad spectrum of frequencies.