85 research outputs found
Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks
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.Comment: Presented at the IEEE International Workshop on Signal Processing
Advances in Wireless Communications (SPAWC) 201
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