  This paper introduces a low-complexity memoryless linearizer for suppression
of distortion in analog-to-digital interfaces. It is inspired by neural
networks, but has a substantially lower complexity than the neural-network
schemes that have appeared earlier in the literature in this context. The paper
demonstrates that the proposed linearizer can outperform the conventional
parallel memoryless Hammerstein linearizer even when the nonlinearities have
been generated through a memoryless polynomial model. Further, a design
procedure is proposed in which the linearizer parameters are obtained through
matrix inversion. Thereby, the costly and time consuming numerical optimization
that is traditionally used when training neural networks is eliminated.
Moreover, the design and evaluation incorporate a large set of multi-tone
signals covering the first Nyquist band. Simulations show
signal-to-noise-and-distortion ratio (SNDR) improvements of some 25 dB for
multi-tone signals that correspond to the quadrature parts of OFDM signals with
QPSK modulation.
