Rain attenuation and worst month statistics verification and modeling for 5G radio link system at 26 GHz in Malaysia

Abstract

The explosive daily dependence on wireless communication services necessitates the research to establish ultrawideband communication systems with ultrahigh bit rate transmission capabilities. The advent of the fifth-generation (5G) microwave link transmitting at millimeter-wave (mm-wave) frequency band is a promising technology to accommodate the escalating demand for wireless services. In this frequency band, however, the behavior of the transmission channel and its climatic properties are a major concern. This is of particular importance in tropical regions where the climate is mainly rainy with large raindrop size and high rainfall rate that may interact destructively with the propagating signal and cause total attenuation for the signal. International Telecommunication Union (ITU) introduced a global rain attenuation model to characterize the effect of rain on the propagating signal at a wideband of frequencies. The validity of this model in tropical regions is still an open question for research. In this paper, real measurements are conducted at Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia, to investigate the impact of rain on the propagation of mm-waves at 26 GHz over the microwave 5G radio link system. Rainfall rate and rain attenuation data sets are collected for one year at one sample per min sampling rate. Both data sets are used to estimate signal propagation conditions in comparison to the ITU model prediction. From the presented results, it is found that at 0.01% percentage of time and rainfall rate of about 120 mm/hr, the propagated signal would experience 26.2 dB losses per kilometer traveled. In addition, there is a significant deviation between the empirical estimation of the worst month parameters and the ITU worst month parameter prediction. Similarly, rainfall rate and rain attenuation estimated through the ITU model imposes a large deviation as compared with the measurements. Furthermore, more accurate empirical worst month parameters are proposed that yielded more accurate estimation of the worst month rainfall and rain attenuation predictions in comparison to the ITU model predictions.