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Deep Learning (DL) has enabled a paradigm shift in wireless communicationsystem with data driven end-to-end (E2E) learning and optimization of thePhysical Layer (PHY). By leveraging the representation learning of DL, E2Esystems exhibit enhanced adaptability and performance in complex wirelessenvironments, fulfilling the demands of 5G and beyond network systems andapplications. The evolution of data-driven techniques in the PHY has enabledadvanced semantic applications across various modalities including text, image,audio, video, and multi-modal transmissions. These applications transcend fromtraditional bit-level communication to semantic-level intelligent communicationsystems, which are capable of understanding and adapting to the context andintent of the data transmission. Although PHY as a DL architecture fordata-driven E2E communication is a key factor in enabling semanticcommunication systems (SemCom), and various studies in recent years havesurveyed them separately, their combination has not been thoroughly reviewed.Additionally, these are emerging fields that are still in their infancy, withseveral techniques having been developed and evolved in recent years.Therefore, this article provides a holistic review of data-driven PHY for E2Ecommunication system, and their enabling semantic applications across differentmodalities. Furthermore, it identifies critical challenges and prospectiveresearch directions, providing a pivotal reference for future development of DLin PHY and SemCom.

Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications