Optimizing Propagation Predictions for Last-mile Connectivity in a CubeSat Perspective

CubeSats is seen as an innovative approach to provide many wireless communications services in a wide footprint coverage manner. However, this could lead to electromagnetic spectrum congestion, which requires larger bandwidths. Thus, optimization approaches and considering of high frequency bands are needed. This work aims to develop an optimized propagation model to enhance the last-mile connectivity of a CubeSat system using an enhanced version of the log-normal shadowing propagation model by considering elevation angle in its formulation. Then, a linear iterative feedback scheme is applied to produce an enhanced approximate solution of the propagation problem. Link budget parameters, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) have been used as performance indictors to validate the proposed scheme. Results show that the optimized model outperforms the non-optimized one from link budget and the two regression metric validation perspectives.