## 2 Related Work  
[PERSON] et al. [2] provided a comprehensive overview of past and current rain attenuation models, outlining classification, accuracy, and comparison with additional details on the LA rain attenuation (LARA) models. [PERSON] et al. [3] suggested antennas with a larger diameter to receive a broader range of data that improves the accuracy of the results and reduces the rain attenuation prediction error. They used a Ka band with an antenna with a diameter of above 7.2 m in tropical regions to prove this. [PERSON] et al. [4] proposed a binary decision probabilistic method for Ka-frequency rain attenuation forecast using a predetermined threshold. The protocol was adjusted for probabilistic weather forecasts in  
Figure 2: The components used for calculating the LB value. a communication satellite control loop. [PERSON] et al. [PERSON] et al. (2018) showed the reduction of the bit error rate for the orthogonal frequency division multiplexing (OFDM) communication link applied for millimeter-waves (MMW, 30 GHz-300 GHz) transmission. [PERSON] et al. [PERSON] et al. (2018) analyzed rain attenuation measurements collected over four years, working at 11, 15, and 19 GHz, and proposed to improve the rain-prediction model's accuracy coefficients. [PERSON] et al. [PERSON] et al. (2018), focused on the distance factor for MMW links, found that the distance values for the path lengths up to 1 km are inconsistent. They proposed a fix for the distance factor in ITU-R P530.17. [PERSON] et al. [PERSON] et al. (2018) describe an omnidirectional and circularly polarized antenna for satellite communication applications. It increases the bandwidth of the antenna by 5-6%. [PERSON] et al. [PERSON] et al. (2018) presented a rain attenuation model for the tropical region with a rainfall rate of 0.01% at the time. It collected input from two satellites operating at Ka-band (WINDS) and Ku-band (GE23), 18.9 GHz and 12.75 GHz, respectively. The gap between the predicted and the actual measures showed that the gap was less than those predicted by the ITU-R, Yamada, DAH, Karasawa, and Ramachandran models. [PERSON] et al. [PERSON] et al. (2018) proposed an improvement of ITU-R for tropical regions and 15 GHz frequency. [PERSON] et al. [PERSON] et al. (2018) offered a combination of rainfall statistics and an ML-based model, which used a neural network trained by rainfall data collected in South Africa for four years. They found that the gap between the predicted and actual results was close, which is better than other models for a short sampling time. [PERSON] et al. [PERSON] et al. (2018) noticed that a rainfall prediction model applied for tropical regions does not generate the same accuracy when applied to other areas. A proper \(\upmu\) and \(\upmu\) model is required to fit other locations, assuming that a large dataset exists for the desired region at different frequencies. Hence, a global model for \(\upmu\) and \(\upmu\) is applicable for any area for a time series of rain attenuation. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) compared rain attenuation prediction in Indonesia using the ITU-R, Global Crane, and DAH models. The study concluded that ITU-R provides the closest forecast. Most of the reviewed models relate to the tropical region while ignoring others. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) used areas of Bangladesh using 36 GHz to 46 GHz and 46 GHz to 56 GHz bands. They observed that the attenuation varied from 40 dB to 170 dB, which is relatively high. Hence, maintaining the connection for 99.99% of the rainy season required 140 dB and 100 dB, respectively. [PERSON], [PERSON] et al. [PERSON] et al. (2018) explored methods to improve the performance and the accuracy of the SNR prediction for LTE and 5G wireless networks and mobile communications. They achieved less than 1 ms process time and complete accuracy when utilizing time-domain signals for SNR range of \((-4,32)\) dB and resolution of 2 dB. [PERSON], [PERSON] et al. [PERSON] et al.