## 2 Related Work  
The alpha-beta-gamma (ABG) model, the floating intercept (FI) model, and the close-in-free space with distance (CI) model were the three basic path-loss models [20; 21]. These models were established using conventional statistical methods or data-dependent machine learning methods. In Ref. [22], machine learning techniques were used to estimate the PL and compare their performance using a random forest, a support vector regression model, and artificial neural networks. The performance of mobile communication systems was calculated and compared using the traditional channel model and the deep learning model at 2.6 GHz [23]. Estimates of propagation loss were made for urban and suburban NLOS scenarios across several frequencies, ranging from 0.8 GHz to 70 GHz [24]. The propagation loss, received power, and PL exponent were estimated for mmWave frequencies using NYUSIM [25]. In an urban LOS scenario at 28 GHz, the PL, PL exponent, and standard deviation were estimated using NYUSIM, which also determined the best direction for signal propagation [26]. Single-frequency CI and FI models and multi-frequency ABG and CIF models were used to evaluate the propagation characteristics of two indoor stairwell environments [27]. The measured results could be utilized for designing an effective indoor communication system and a small-cell wireless network. In Ref. [28], a comparison and analysis of various path-loss models were presented. A PL measurement campaign was conducted in New York City and Austin at 28 GHz and 38 GHz in a UMi environment [29]. From the measurement, it was identified that the shadow factor reduced the PL by 1 dB in New York City and 6 dB in Austin. The improved versions of the CI and FI PL models were considered to measure the mean prediction and standard deviation error for vertical-horizontal and vertical-vertical antenna polarization [21]. The results confirmed that the new versions of the CI and FI models provided better PL compared to the conventional models. In Ref. [30], the authors compared empirical path-loss models with practical measurements observed at a frequency of 3.5 GHz in Cambridge, UK. They identified that the ECC-33 models produced optimized path loss compared to the Hata and SUI models. In Ref. [31], the authors estimated the path loss using the Hata model and compared it with outdoor measurements. From the comparison, the authors identified the best-optimized path-loss model that yielded the lowest relative error. In Ref. [32], three path-loss models were used to predict the path loss and were compared with the measured data. The authors determined that the Hata model was the best model for path loss prediction in the urban environment. In Ref. [33], the authors conducted measurements of the LOS path loss at frequencies of 3.35 GHz, 8.45 GHz, and 15.75 GHz using the break point distance. Based on the break point distance, they proposed two path loss formulas, one for the lower bound and another for the upper bound of LOS paths in urban micro environments. In mmWave communication systems, beam management was the major problem due to dense network deployment and directional transmission. Many authors addressed beam management algorithms in the literature to enhance the wireless communication system performance. In Ref. [34], an adaptive beam management algorithm was proposed to enhance privacy protection and to reduce resource conservation. In Ref. [35], hybrid beam-forming scheme was proposed. In this method mmWave spectrum was shared between the multi-beam satellite system and cellular system and maximized the secrecy energy efficiency of the proposed system. In Ref. [36], an optimization scheme was used to maximize the secrecy energy efficiency. The proposed method will enhance security transmission and reduce power consumption. In Ref. [37], the authors proposed a joint beam-forming scheme and optimization scheme for hybrid satellite relay networks to minimize the total transmit power and to enhance the secrecy energy efficiency.