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- W4380591520 abstract "Modern radio engineering systems often operate in a complex and dynamically changing signal-interference environment. Adaptive antenna arrays (AAAs) in these systems perform interference suppression, as a rule, using classical quasioptimal algorithms based on linear algebra methods. Such algorithms do not always effectively suppress non-stationary interference, especially in a practical implementation. This research focuses on the development and application features of the adaptation algorithm based on a recurrent neural network for a digital AAA. The results of numerical simulation show that the LSTM neural network provides comparable efficiency of suppression of non-stationary noise and pulse interference with the classical adaptation algorithm." @default.
- W4380591520 created "2023-06-15" @default.
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- W4380591520 date "2023-05-29" @default.
- W4380591520 modified "2023-09-27" @default.
- W4380591520 title "Recurrent Neural Network Application Features to Suppress Non-stationary Interference in an Adaptive Antenna Array" @default.
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- W4380591520 doi "https://doi.org/10.1109/weconf57201.2023.10147941" @default.
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