  In this study we present how to approach the problem of building efficient
detectors for spectrally efficient frequency division multiplexing (SEFDM)
systems. The superiority of residual convolution neural networks (CNNs) for
these types of problems is demonstrated through experimentation with many
different types of architectures.
