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.


Published in:
2018 Ieee 19Th International Workshop On Signal Processing Advances In Wireless Communications (Spawc), 1-5
Presented at:
IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, GREECE, Jun 25-28, 2018
Year:
Jan 01 2018
Publisher:
New York, IEEE
ISSN:
2325-3789
ISBN:
978-1-5386-3512-4
Keywords:
Laboratories:
TCL




 Record created 2018-12-13, last modified 2020-10-25


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