Deep Learning for Signal Processing in Communication Systems

Abstract

The significant demands placed on contemporary communication systems have prompted a reassessment of the established and comprehensive communication theory. The obsolescence of present system implementations is contentious, yet they may adapt to problems when integrated with new technology. The concept of formulating wholly new methodologies has also emerged. Recent advancements in machine learning, particularly in deep learning methodologies, provide potential new avenues for study. This study elucidates the rationale for the incorporation of deep learning in communications.
The report delineates the present research methodologies. Emphasis is placed on examining many domains of appliances and delineating their merits and demerits, including the development of comprehensive communication systems such as autoencoders and the role of neural networks in signal recognition and modulation categorization. The findings suggest significant promise for deep learning applications in communications inside suboptimal domains.