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ENERGY EFFICIENCY OF NEURO-FUZZY SELECTION OF MODULATION METHODS IN SPACE TELECOMMUNICATIONS SYSTEMS OF GEOECOLOGICAL REMOTE SENSING OF THE EARTH
Abstract
Modern space complexes are designed to solve a wide range of tasks, including remote sensing of the Earth in order to assess the geoecological state of territories. The transmission of super-resolution images obtained as a result of sensing to ground-based control systems is traditionally carried out by space telecommunications systems, the requirements for technical characteristics and parameters of which in terms of the speed of information exchange and the reliability of communication are constantly being tightened. This forces developers to look for new approaches to implementing the OSI network model at the physical data transfer layer. The studies conducted by the authors have shown the prospects of adaptive changes in the modulation methods and encoding speed when varying the signal-to-noise ratio of the receivers to maintain an acceptable level of the bit error probability value. For the qualitative implementation of these solutions for the adaptation of the physical layer of the network model of the OSI, the use of modern approaches based on machine learning technology and automated decision-making mechanisms is proposed. Among them, in particular, are fuzzy-logic algorithms and artificial neural networks that have ultra-high decision-making speed and high resistance to noise in the input data, which is extremely important when transmitting data by space telecommunications systems. To determine the potential for adaptation of the physical layer, the paper quantifies the energy efficiency of the most common digital modulation methods in practice, which include PSK, FSK, and QAM.
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