Observation of site diversity gain dependency on separation distance using an attenuation-dependent logarithmic model in a tropical region

• Main Authors: Samat, Fadzliana, Singh, Mandeep Jit, Sali, Aduwati, Maniam, P.M.Kalai Vaanan
• Format: Article
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
• Online Access: http://psasir.upm.edu.my/id/eprint/94404/; https://ieeexplore.ieee.org/document/9343821
• Institution: Universiti Putra Malaysia

Ensuring site diversity is a fade mitigation technique used to overcome severe rain-induced attenuation encountered in high-frequency signal communications, especially over tropical regions. However, due to the high cost of deploying diverse equipment and terrestrial infrastructures for connections between two sites, gain prediction models are widely used to evaluate the performance of a given scheme. This article presents a study on the behavior of four parameters that contribute to site diversity gain: site separation distance, link frequency, elevation angle, and baseline orientation angle. A correlation between gain and distance is found in the form of an attenuation-dependent logarithmic function instead of the gain being saturated when the increment of the distance exceeds the convective rain cell extent, as was found in the literature. Therefore, a site diversity logarithmic model, SDLog, is proposed. The analysis utilized seven site diversity experiments conducted in the tropics, i.e., in Malaysia, Singapore, and Guam, USA. The performance of the SDLog model was compared with the performances of existing models, namely, the ITU-R, Hodge, Panagopoulos, Semire, and X. Yeo models. The SDLog model reproduces the experimental datasets with an average root mean squared error (RMSE) of 0.12, while those of the ITU-R, Hodge, Panagopoulos, Semire, and X. Yeo models are 0.226, 0.289, 0.19, 0.192, and 0.311, respectively. These models were also evaluated based on diversity experiments conducted in Indonesia, separate from the former evaluation. The RMSE and mean absolute percentage error (MAPE) of all evaluations are presented, and the SDLog model seems to be convincing.