Enhanced adaptive code modulation for rainfall fade mitigation in Ethiopia

Abstract Rain attenuation is considerably noticed in a frequency spectrum above 7-GHz for tropical equatorial regions and in a frequency spectrum higher than 10-GHz for temperate climates. The attenuation prediction method provided by the International Telecommunication Union-Recommendation (ITU-R), through Recommendation P.530-16 and P.618-13 utilize data collected from temperate regions. Since the average raindrop size is bigger and the rainfall rate is high in magnitude in tropical regions than that of non-tropical areas, this prediction model is not suitable for the measured rain data. Unfortunately, a rain fade mitigation technique based on local rain data has not been adequately studied in tropical regions. This paper presents an enhanced adaptive code modulation (ACM) for rainfall fade mitigation in Ethiopia. In this research work, locally collected one-minute rain rate data is used to determine the rain attenuation. Then based on this result, the neuro-fuzzy inference system is employed to enhance the mitigation technique. Furthermore, a comparison of the performance of this proposed scheme is with the non-adaptive technique, and fuzzy-based adaptive modulation and coding technique is carried out. MATLAB simulation result showed that lower-order quadrature amplitude modulation (QAM) scheme with a lower convolutional coding rate is better in maintaining link availability in bad weather conditions. However, spectral efficiency is improved by utilizing a larger constellation size of quadrature amplitude modulation (QAM) scheme with a higher convolutional coding rate when the channel is not affected by rain.