# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
###### Abstract  
The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuation. 5G networks; millimeter wave; LOS probability; path loss; link margin; optimization +
Footnote †: journal: _Sciences_  
communication, the signal strength relies on the type of propagation medium [5]. The transmission of radio waves at millimeter wavelengths can be significantly hampered by rain-induced attenuation, which restricts path length and hinders the use of higher frequencies for LOS communication. Heavy raindrops absorb or scatter radio waves, leading to a reduction in the received signal strength. Raindrop absorption and scattering in mmWave frequency bands result in substantial transmission losses [6]. The extent of attenuation or transmission losses varies based on factors such as path length, polarization, frequency, temperature, and latitude. In Malaysia, wireless channels are notably affected by frequent and heavy rainfall [7]. Signal attenuation during propagation depends on parameters such as the rainfall rate, rain density, raindrop size, signal operating frequency, path length, and air quality [8; 9]. Various physical and empirical models can be utilized to forecast rain attenuation [10]. These models take into account local environmental conditions and operating frequencies. Rain attenuation at mmWave frequencies can be estimated using models such as the ITU-R 530-16 model, [PERSON] model, [PERSON]'s model, [PERSON] model, [PERSON] model, and differential equation model [11]. Signal attenuation increases with higher rainfall rates, signal absorption, and path differences. These factors lead to path loss and constrain the coverage area. The path loss denotes the reduction in effective transmit power of a wireless signal as it traverses the channel. It is defined as the loss of signal energy during propagation from transmitter to receiver. This path is influenced by various factors, including path differences, both small and large obstructions, climatic conditions, and antenna characteristics. The antenna's location, tilt angle, and height determine its directivity, which is used to estimate the path loss [12]. Path loss can be estimated using LOS and NLOS prediction models. These models are further categorized into indoor and outdoor models, as well as long-distance and short-distance models. The literature extensively discusses several path loss models [13; 14; 15; 16; 17]. In [13], the influence of rainfall intensity on wave propagation at 26 GHz was measured and compared among all the states in Malaysia. Rainfall attenuation increases the overall path loss. In certain tropical regions, sand and dust particles attenuate propagating signals. The impact of dusty storms on electromagnetic waves was assessed using the Mie scattering model [18].