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- W4320031188 abstract "An unsupervised learning-based blind RF scene analysis method is proposed. The method can analyze a complex radio scene containing a mixture of different transmission types and estimate the constituent signals with associated channel vectors from multi-antenna measurements. A deep neural network is trained to learn the unique time-frequency patterns of various signal types. The channels, noise powers, and encodings input to the neural network are estimated in a maximum likelihood framework via an expectation-maximization algorithm. Numerical tests using scenes constructed from real RF measurements verify the effectiveness of the proposed method." @default.
- W4320031188 created "2023-02-12" @default.
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- W4320031188 date "2022-11-28" @default.
- W4320031188 modified "2023-09-30" @default.
- W4320031188 title "Unsupervised Radio Scene Analysis Using Neural Expectation Maximization" @default.
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- W4320031188 doi "https://doi.org/10.1109/milcom55135.2022.10017594" @default.
- W4320031188 hasPublicationYear "2022" @default.
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