Deep Learning Based Detection for Spectrally Efficient FDM Systems

Abstract

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.