Christina Auer, Thomas Paireder, Oliver Lang, Mario Huemer,
"Kernel Recursive Least Squares Based Cancellation of Second-Order Intermodulation Distortion"
: Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2020), IEEE, 11-2020, ISBN: 978-0-7381-3126-9
Original Titel:
Kernel Recursive Least Squares Based Cancellation of Second-Order Intermodulation Distortion
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2020)
Original Kurzfassung:
Due to the non-ideal components in the analog front-end of frequency division duplex transceivers a part of the transmit signal leaks into the receive path. Together with nonlinear effects in the receiver this leads to self-interferences with possibly high power levels. An important example is the second-order intermodulation distortion (IMD2), since it occurs independently of the transmit carrier frequency. Model based adaptive filtering algorithms are one way to mitigate the IMD2
interference. In this work, we investigate kernel adaptive filters for self interference cancellation, which do not need a model of the specific interference. We focus on a particular variant, the kernel recursive least squares (KRLS). We compare the cancellation performance of this algorithm to recently published nonlinear adaptive filter that are tailored to the IMD2 problem. It turns out that the KRLS clearly outperforms the model based approach, especially for high transmit power levels.
Forschungs-