Abstract

Autoencoders have become a prominent focus in unsupervised learning research due
to their ability to capture essential data features, perform efficient dimensionality
reduction and aid in noise reduction. In this paper, we propose a novel approach
that integrates autoencoders with deep learning classifiers for the efficient reception
of Binary Phase Shift Keying signals. Specifically, three distinct autoencodersLin-
ear, Long Short-Term Memory, and Convolutionalare cascaded with deep learning
classifiers to denoise received signals corrupted by Additive White Gaussian Noise.
This streamlined methodology not only enhances signal quality and interpretability
but also facilitates the development of a more efficient receiver, outperforming con-
ventional designs that rely on multiple pr

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