Christina Auer, Thomas Paireder, Mario Huemer,
"Kernel Recursive Least Squares Algorithm for Transmitter-Induced Self-Interference Cancellation"
: Proceedings of the Vehicular Technology Conference (VTC Spring 2021), IEEE, 4-2021, ISBN: 978-1-7281-8964-2
Original Titel:
Kernel Recursive Least Squares Algorithm for Transmitter-Induced Self-Interference Cancellation
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Vehicular Technology Conference (VTC Spring 2021)
Original Kurzfassung:
In order to enable frequency-division duplex operation, radio frequency transceivers usually employ a spectral isolation between transmitter and receiver. Due to nonidealities of the used duplexer filters, the transmit signal leaks into the receive path. Although operating on different frequency bands, nonlinear effects in the transceiver may lead to self-interferences with possibly high power levels. One approach to restore the receiver signal-to-noise ratio in these cases is to apply a digital cancellation of the interference. If perfect model knowledge is available, particularly tailored algorithms can be used for interference cancellation. In this work, we apply a kernel-based universal estimation algorithm, in particular the kernel recursive least squares (KRLS) algorithm, to cancel two different nonlinear interference effects. The transmitter-induced harmonics are explained and studied in detail, while the receiver-induced intermodulation distortion has been treated in a second paper explicitly. The KRLS algorithm is able to cancel both, while two different model-based and particularly tailored methods would be needed to address the two fundamentally different interference effects.
Forschungs-