Deep Learning in Physical Layer: Review on Data Driven End-to-End
  Communication Systems and their Enabling Semantic Applications
Nazmul Islam Seokjoo Shin
Deep Learning (DL) has enabled a paradigm shift in wireless communication
system with data driven end-to-end (E2E) learning and optimization of the
Physical Layer (PHY). By leveraging the representation learning of DL, E2E
systems exhibit enhanced adaptability and performance in complex wireless
environments, fulfilling the demands of 5G and beyond network systems and
applications. The evolution of data-driven techniques in the PHY has enabled
advanced semantic applications across various modalities including text, image,
audio, video, and multi-modal transmissions. These applications transcend from
traditional bit-level communication to semantic-level intelligent communication
systems, which are capable of understanding and adapting to the context and
intent of the data transmission. Although PHY as a DL architecture for
data-driven E2E communication is a key factor in enabling semantic
communication systems (SemCom), and various studies in recent years have
surveyed them separately, their combination has not been thoroughly reviewed.
Additionally, these are emerging fields that are still in their infancy, with
several techniques having been developed and evolved in recent years.
Therefore, this article provides a holistic review of data-driven PHY for E2E
communication system, and their enabling semantic applications across different
modalities. Furthermore, it identifies critical challenges and prospective
research directions, providing a pivotal reference for future development of DL
in PHY and SemCom.