The new era of space exploration demands a significant increase in the number of human and robotic missions, thus resulting in novel communication and service requirements. To satisfy such requirements, the fifth generation of mobile communication systems (5G), despite providing connectivity on Earth, has the potential to serve as a communication standard for space resource missions, particularly the ones targeting the Moon. In fact, 5G non-terrestrial networks (NTNs) are already in the standardization process and new techniques are being proposed in order to counteract the peculiarities of the non-terrestrial channel. However, going one step ahead and deploying constellations of satellites around the Earth or the Moon, requires first a detailed analysis and testing of the validity of the proposed techniques. Therefore, in this paper, we introduce the 5G Space Communications Lab, which has been developed with the purpose of simulating space-based 5G communications. The designed testbed proposed here increases the technology readiness level (TRL) of NTN-based 5G systems, demonstrating over a laboratory environment successful 5G communication via space links.

Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these approaches ultimately depends on the realism of the physical model, and many works only concentrate on everyday photography. In this work we present a denoising approach for extremely low-light images of permanently shadowed regions (PSRs) on the lunar surface, taken by the Narrow Angle Camera on board the Lunar Reconnaissance Orbiter satellite. Our approach extends existing learning-based approaches by combining a physical noise model of the camera with real noise samples and training image scene selection based on 3D ray tracing to generate realistic training data. We also condition our denoising model on the camera’s environmental metadata at the time of image capture (such as the camera’s temperature and age), showing that this improves performance. Our quantitative and qualitative results show that our method strongly outperforms the existing calibration routine for the camera and other baselines. Our results could significantly impact lunar science and exploration, for example by aiding the identification of surface water-ice and reducing uncertainty in rover and human traverse planning into PSRs.