Nowadays, the world is turning into technology, fast internet and high signal quality. To ensure high signal quality, the network planners have to predict the pathloss and signal strength of the transmitted signal at specific distances in the design stage. The aim of this research is to provide a generalized pathloss model to suit the urban area in Muscat Governorate in the Sultanate of Oman. The research covers 5G network pathloss in the Muttrah Business District (MBD) area. It includes Close In (CI) model and Alpha Beta Gamma (ABG) model with 3.45GHz. The results of 5G models were compared with real experimental data in MBD by calculating Root Mean Square Error RMSE. Other cells at MBD area were used for reverification. To validate the modified pathloss models of 5G, they were applied at different cells in Alkhoud area. Furthermore, this paper also deals the effect of Specific Absorption Rate (SAR) on the human brain for ensuring safety due to close proximity to cell towers. The SAR values were calculated indirectly from the electric field strength of different antennas. Calculated results were compared with the international standards defined limits on the human brain.

The topic of incompressible fluid flow in rough channels is of practical interest in many diverse applications. It also forms the basis of our understanding of fluid-wall interactions, turbulent eddy generation, and their effect on the frictional pressure losses. Although this topic is also of fundamental interest, the work in this area is entirely guided by the experimental work of earlier investigators [1–6]. The works by Nikuradse [4] and Colebrook [5] constitute a major milestone from which useful empirical models are derived. As we approach the microscale, Nikuradse’s experimental work again is brought to focus, perhaps this time to gain an insight into the mechanisms affecting fluid-wall interaction in rough channels. In this paper, Nikuradse’s work is revisited in light of the recent experimental work on roughness effects in microscale flow geometries.