  Radio frequency transceivers operating in in-band full-duplex or
frequency-division duplex mode experience strong transmitter leakage. Combined
with receiver nonlinearities, this causes intermodulation products in the
baseband, possibly with higher power than the desired receive signal. In order
to restore the receiver signal-to-noise ratio in such scenarios, we propose two
novel digital self-interference cancellation approaches based on spline
interpolation. Both employ a Wiener structure, thereby matching the baseband
model of the intermodulation effect. Unlike most state-of-the-art spline-based
adaptive learning schemes, the proposed concept allows for complex-valued in-
and out-put signals. The optimization of the model parameters is based on the
stochastic gradient descent concept, where the convergence is supported by an
appropriate step-size normalization. Additionally, we provide a gain control
scheme and enable pipelining in order to facilitate a hardware implementation.
An optional input transform improves the performance consistency for correlated
sequences. In a realistic interference scenario, the proposed algorithms
clearly outperform a state-of-the-art least mean squares variant with
comparable complexity, which is specifically tailored to second-order
intermodulation distortions. The high flexibility of the spline interpolation
allows the spline-based Wiener models to match the performance of the kernel
recursive least squares algorithm at less than 0.6% of the arithmetic
operations.
