The 2017 National Academy of Sciences Decadal Survey highlighted several high priority objectives to be pursued during the next decadal timeframe, and the next-generation Cloud Convection Precipitation (CCP) observing system is thereby contemplated. In this study, we investigate the capability for ice cloud remote sensing of two CCP candidate observing systems that include a W-band cloud radar and a submillimeter-wave radiometer by developing hybrid Bayesian algorithms for the active-only, passive-only, and synergistic retrievals. The hybrid Bayesian algorithms combine the Bayesian MCI and optimization process to retrieve quantities and uncertainty estimates. The radar-only retrievals employ an optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. The a priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. Simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with the known values. The experiment results suggest that the radiometer measurements provide little information on the vertical distributions of ice cloud microphysics. Radar observations have better capacity for retrieving water content compared to particle number concentration. The synergistic information is demonstrated to be helpful in improving retrieval accuracies, especially for the ice water path retrievals. The end-to-end simulation experiments also provide a framework that could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies.