This paper presents the large-scale parameters of narrowband multipath channel propagation of corridor environments for the fifth generation (5G) communications system. The measurements were conducted on narrow, wide and open corridor structure in Universiti Teknologi Malaysia (UTM) Kuala Lumpur campus, Malaysia. Measurements campaign are conducted at 6.5 GHz and 10.2 GHz , and path loss exponents (PLEs) and shadow fading are extracted using linear-least-square regression fitting from measured received-signal-strength (RSS) . Furthermore, the results measurements campaign is modeled using well-known closein reference distance (CI) and floating-intercept (FI) models. The result found in this work discovered the breakpoint distances (bps) of radio propagation are seen varies differently at those corridors for diverse frequencies.

The impact of atmospheric attenuation on wireless communication links is much more severe and complicated in tropical regions. That is due to the extreme temperatures, intense humidity, foliage and higher precipitation rain rates with large raindrop sizes. This paper investigates the propagation of the mm-waves at the 38 GHz link based on real measurement data collected from outdoor microcellular systems in Malaysia. The rainfall rate and received signal level have been measured simultaneously in 1-minute time intervals for one year over a 300 m path length. The rain attenuation distributions at different percentages of exceedance time have been compared with the modified distance factor of the ITU-R P.530-17 model. The average link availability calculated with the measured rain rates has been analysed. Additionally, the key propagation channel parameters such as the path loss, path loss exponent, Rician K-factor, root mean square, delay spread and received power have been investigated considering the rain attenuation. These propagation channel parameters have been analysed using MATLAB software and explained with the help of the latest NYUSIM channel model software package (Version 2.0). The analysis results have been classified considering rain attenuation, antenna setup, link distances, antenna height and antenna gain. The outcomes revealed that the rain fade predicted by applying the modified distance factor provides high consistency with the measured fade in Malaysia and several available measurements from different locations. The large-scale path loss model in the NYUSIM simulation result was around 126.23 dB by considering the rain attenuation effects on the 300m path length. This work shows that the NYUSIM channel model offers more accurate rendering results of path loss for omnidirectional and directional antenna transmissions without rain fade. This study proves that the ability to provide good coverage and ultra-reliable communication for outdoor and outdoor-to-indoor applications during rain in tropical regions must be sufficiently addressed.

Multi-access edge computing (MEC) is a key technology in the fifth generation (5G) of mobile networks. MEC optimizes communication and computation resources by hosting the application process close to the user equipment (UE) in network edges. The key characteristics of MEC are its ultra-low latency response and real-time applications in emerging 5G networks. However, one of the main challenges in MEC-enabled 5G networks is that MEC servers are distributed within the ultra-dense network. Hence, it is an issue to manage user mobility within ultra-dense MEC coverage, which causes frequent handover. In this study, our purposed algorithms include the handover cost while having optimum offloading decisions. The contribution of this research is to choose optimum parameters in optimization function while considering handover, delay, and energy costs. In this study, it assumed that the upcoming future tasks are unknown and online task offloading (TO) decisions are considered. Generally, two scenarios are considered. In the first one, called the online UE-BS algorithm, the users have both user-side and base station-side (BS) information. Because the BS information is available, it is possible to calculate the optimum BS for offloading and there would be no handover. However, in the second one, called the BS-learning algorithm, the users only have user-side information. This means the users need to learn time and energy costs throughout the observation and select optimum BS based on it. In the results section, we compare our proposed algorithm with recently published literature. Additionally, to evaluate the performance it is compared with the optimum offline solution and two baseline scenarios. The simulation results indicate that the proposed methods outperform the overall system performance.