## 1 Introduction  
The increasing demand for mobile data in daily life, as well as the importance of high-speed data transmission, has led to enormous growth of mobile communication systems. To a large extent, mobile communication systems have witnessed major advances in wireless access, integrated circuits, and digital signal processing, which have brought about additional technologies such as Global positioning system (GPS), IP telephony, online gaming, streamed multimedia, and ultra-broadband internet access [1].  
There are two primary limitations to the performances of the 4G mobile communication systems: time and location. The new 5G technology, with 28 and 38 GHz mm wave frequencies, address these limitations [2]. Amongst the channel characteristics, signal paths change is the most critical parameter. The path loss is defined as a decrease in the signal strength during propagation from the transmitter to the receiver [3]. Several effects may cause the path loss, such as reflection, refraction, diffraction, absorption, coupling, and cable loss [4]. The path loss inside the building is also influenced by many factors, including the propagation medium, environment, height of the antennas, and the distance between the transmitter and the receiver inside the building [5; 6]. Figure 1 shows the effect of path loss on the received signal power over an urban area and inside building obstacles. However, there is a clear relationship between the signal strength and the temperature. In general, when the temperature increases, the Received Signal Strength Indicator (RSSI) drops [7]. Naturally, the relative humidity increases and drops based on RSSI, which is dependent on a positive relationship with the trend of Absolute Humidity (AH), indicating a negative correlation [8]. If the size of a window is large the signal will be high; if the window size decreases, the signal will decrease [9; 10].  
Radio channels are random. In order to predict path loss, the radio channels must be modeled [4]. Therefore, for practical path loss prediction, propagation models are used. Among these models are the Okumura, COST-Hata, and empirical models, which are all used extensively. Path loss calculation is typically done based on observations and data measurements, such as the distance of the receiver from the transmitter and signal frequency. These two measures are manipulated in a logarithmic format as given in Equation (1).  
\[PL=32.45+20\log 10(d)+\log 10(f) \tag{1}\]  
**Definition 1**.: _(PL) is the path loss, (d) is the distance between the sender (TX) and the receiver (RX), and (f) is the frequency, the (32.45) is the coefficient. This model is the basic model for all path loss models._  
Among the most broadly used empirical models are the Okumura model and COST-Hata model [4]. Okumura model is applicable in the frequency range 3 GHz [3; 5], whereas COST-Hata model gives simple and straightforward techniques to ascertain path loss in a wider frequency range. Despite the fact that working frequency (5 GHz) is well beyond its estimation extent, its effortlessness and adjustment variables still allow for the calculation of path loss occurring at higher frequencies. The Okumura and COST-Hata models are an accepted standard of today's propagation prediction models, but both models do not consider certain pertinent effects, such as indoor and outdoor environments, humidity, temperature, and window size. This paper proposes a path loss propagation model that illuminates new indicators like windows size and weather conditions. Thus, our findings help telecommunication providers in network planning to design stable mobile indoor networks that work accurately with the high and low power signal.  
The remainder of this paper is structured as follows: Section 2 presents related work for path loss models; Section 3 presents details on the analysis methodology of the proposed model; Section 4 demonstrates the evaluation and results of the proposed model Section 5 concludes this paper by considering the potential usefulness of the model system.