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Cognitive algorithms for communications systems have been presented in literature, but very few have been integrated into a fielded system, especially space communications systems. In this paper, we describe the implementation of a multi-objective reinforcement-learning algorithm using deep artificial neural networks acting as a radio-resource-allocation controller. The developed software core is generic in nature and can be ported readily to another application. The cognitive engine algorithm implementation was characterized through a series of tests using both a ground-based system and a space-based system. The ground system comprised of engineering-model software-defined radios, commercial modems, and RF equipment emulating the targeted space-to-ground channel. The on-orbit communication system, including a space-based, remotely controlled transmitter, resides on the International Space Station and operates with a ground-based receiver at NASA Glenn Research Center. Through a series of on-orbit tests, the cognitive engine was tested in a highly dynamic channel and its performance is discussed and analyzed.
Hackett et al. (2018) studied this question.