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Although communications-based cognitive engines have been proposed, very few have been implemented in a full system, especially in a space communications system. In this paper, we detail the implementation of a multi-objective reinforcement-learning algorithm and deep artificial neural networks for the use as a radio-resource-allocation controller. The modular software architecture presented encourages...
Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications...
Large-scale natural disasters present complex challenges for disaster relief communications. Fixed infrastructures, such as cell towers or radio base stations, may be completely destroyed during a disaster or this infrastructure may never have existed. In a disaster situation, having unreliable communications systems can put a relief personnel's safety at risk as well as make the effort much less...
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