“…A residual SI signal generally remains after analog cancellation, which must be canceled in the digital domain. This digital cancellation requires modeling the non-linear distortion and memory effects, such as digital-to-analog converter (DAC) and analog-to-digital-converter (ADC) non-linearities [5], IQ imbalance [5], [6], phase-noise [7], [8], and power amplifier (PA) non-linearities [4]- [6], [9] in addition to the memory effects of the channel as illustrated in Fig 1 . To describe and adjust the parameters of these effects various methods have been proposed such as polynomial models [4], [6], [10], [11], support vector machines [12], neural networks (NNs) [13]- [15], and more traditional machine learning methods [16]. However, while the cancellation performance and complexity of time-invariant systems have already been investigated thoroughly, we note that the parameters of FD systems change over time due to, e.g., circuit temperature variations and motion in the surrounding environment.…”