  In this paper, we propose a multi-layer artificial neural network (ANN) that
is trained with the Levenberg-Marquardt algorithm for use in signal detection
over multiple-input multiple-output orthogonal frequency-division multiplexing
(MIMO-OFDM) systems, particularly those with low-resolution analog-to-digital
converters (LR-ADCs). We consider a blind detection scheme where data symbol
estimation is carried out without knowing the channel state information at the
receiver (CSIR)---in contrast to classical algorithms. The main power of the
proposed ANN-based detector (ANND) lies in its versatile use with any
modulation scheme, blindly, yet without a change in its structure. We compare
by simulations this new receiver with conventional ones, namely, the maximum
likelihood (ML), minimum mean square error (MMSE), and zero-forcing (ZF), in
terms of symbol error rate (SER) performance. Results suggest that ANND
approaches ML at much lower complexity, outperforms ZF over the entire range of
assessed signal-to-noise ratio (SNR) values, and so does it also, though, with
the MMSE over different SNR ranges.
