Christina Auer, Thomas Paireder, Mario Huemer,
"Self-Interference Cancellation in LTE/5G Transceivers with Sliding Window Kernel Recursive Least Squares Filters"
: Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2021), IEEE, Seite(n) 970-976, 11-2021, ISBN: 978-1-6654-5828-3
Original Titel:
Self-Interference Cancellation in LTE/5G Transceivers with Sliding Window Kernel Recursive Least Squares Filters
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2021)
Original Kurzfassung:
State-of-the-art radio frequency (RF) transceivers for LTE-A/5G mobile communication devices typically operate in frequency division duplex mode. Because of the non-ideal duplexing filters, parts of the transmit signal leak into the receive paths. In combination with non-idealities in the analog blocks of the transceiver, this leakage might cause so-called selfinterference (SI). Typically, model-based adaptive filters are used for the cancellation. However, with the increasing number of SI effects, approaches that are able to learn the interference from data, such as kernel adaptive filter, are an interesting option. The main problem of kernel adaptive filter (KAF) is
that without a proper sparsification technique they suffer from unbounded computational cost. In this paper, we investigate the sliding-window kernel recursive least squares (SW-KRLS) algorithm, which features fast tracking, while ensuring limited cost. We demonstrate the excellent performance of the SW-KRLS algorithm for time-varying SI problems in RF transceivers.
Forschungs-