Microwave propagation for deep space exploration: modeling radio-meteorological effects and optimizing data volume transfer

Microwave propagation is strongly influenced by Earth atmosphere: especially for frequencies above 10 GHz, tropospheric effects may strongly degrade microwave propagating signals. These effects, and more in general radio-meteorological effects of the atmosphere on a propagating electromagnetic signal, can be modeled to allow the computation of the attenuation experienced by the signal. In the case of deep space missions, we have a space-to-Earth communication (data transfer) between a spacecraft and an Earth station. One of the main objective is to prevent, or keep as limited as possible, any data loss to ensure a high received data volume (DV). Weather forecast (WF) models can be used for this purpose, thanks to their capability of modeling and predicting the atmospheric state. In this work, we propose a WF-based approach for DV predictions that combines a numerical model for WF with a radio-propagation model. The WF model provides a prediction of the atmospheric state (for the period in which the space-to-Earth transmission is expected) and the radio-propagation model converts this atmospheric state into radio-propagation parameters by solving the radiative transfer problem. Finally, the radio-propagation parameters are used to compute the expected received and lost DV during the space-to-Earth transmission. The DV estimation is accomplished through the link-budget computation. Due to the much larger atmospheric uncertainties, classical link-budget methods (which make use of climatological atmospheric statistics on a monthly, annual or seasonal time-scale obtained collecting measured data for several years) are not suitable at high microwave frequencies (above 10 GHz). In this work, we introduce a stochastic approach, for microwave interplanetary links, based on the maximization of the data return in an average sense with the atmospheric loss being the driving random variable. Exploiting WF-models allow using atmospheric statistics coming from short-term WF made for a period that coincides with the transmission period. As a baseline for testing and validating the proposed WF-based approach for DV predictions, the BepiColombo ESA mission to Mercury is chosen. The mission, planned to start in 2017, considers the use of a Ka-band (32 GHz) channel for the downlink segment. Different prediction methods are introduced and tested in different scenarios. Results are shown in terms of received and lost DV and compared with benchmark (ideal) cases. Using daily statistics coming from short-term WF, yearly DV return can be increased more than 20% with respect to the case when monthly climatological statistics are used (which is the standard procedure). We have extended the Ka-band results to other frequencies and we have studied the potentials of using frequencies up to W band (about 75 GHz) for deep space links. The advantages of using higher frequencies are not only linked to the higher received DV but also to the possibility of transmitting more data: a wider bandwidth allows using higher transmission data rates.