AI-Based Investigation and Mitigation of Rain Effect on Channel Performance With Aid of a Novel 3D Slot Array Antenna Design for High Throughput Satellite System

Abstract

Rain attenuation poses a significant challenge for high-throughput communication systems. In response, this paper introduces an artificial intelligence (AI) model designed for predicting and mitigating rain-induced impairments in high-throughput satellite (HTS) to land channels. The model is based on three AI algorithms developed using 3D antenna design to characterize, analyze, and mitigate rain-induced attenuation, optimizing channel quality specifically in the United Arab Emirates (UAE). The study evaluates various parameters, including rain-specific attenuation, effective slant path through rain, rain-induced attenuation, signal carrier-to-noise ratio, and symbol error rate, for five conventional modulation schemes: Quadrature Phase-Shift Keying (QPSK), 8-Phase Shift Keying (8-PSK), 16-Quadrature Amplitude Modulation (16-QAM), 32-QAM, and 64-QAM. Additionally, the paper introduces a new database detailing rain-induced attenuation in HTS channels in the UAE at different frequencies using measured rainfall intensities. The paper concludes by proposing a smart antenna design with a frequency diversity technique for fade mitigation. Results indicate that rain-induced attenuation varies significantly based on rainfall rate and frequency. Specifically, at 25 GHz and a rainfall rate of 100 mm/h, the rain-induced attenuation can reach as high as 15 dB, resulting in a significant decline in signal quality and link performance. The