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- W2963514534 abstract "Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions. Measurement results from a full-duplex testbed demonstrate that a small and simple feed-forward neural network canceler works exceptionally well, as it can match the performance of the polynomial non-linear canceler with significantly lower computational complexity." @default.
- W2963514534 created "2019-07-30" @default.
- W2963514534 creator A5075502847 @default.
- W2963514534 date "2018-06-01" @default.
- W2963514534 modified "2023-10-01" @default.
- W2963514534 title "Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks" @default.
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- W2963514534 doi "https://doi.org/10.1109/spawc.2018.8445987" @default.
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