ZTE Communications Special Issue on
Native Intelligence at the Physical Layer
The concept of native intelligence at the physical layer (PHY) represents a novel approach to enhancing performance of mobile communication systems such as 6G. By embedding advanced algorithms and AI-driven solutions directly into the PHY, it is possible to improve the performance, reliability, and efficiency of communication systems. Intelligent algorithms have the potential to adapt in real-time to varying channel conditions, enhance spectral efficiency, and ensure robust communication even in complex environments. These algorithms show significant promise in areas such as adaptive signal processing, improved channel estimation, and robust error correction. However, practical implementation poses challenges, such as the need for substantial data for model training, the computational power required for real-time processing, and ensuring the adaptability of algorithms across diverse scenarios.
This special issue aims to gather cutting-edge research and developments in the field of native intelligence at the physical layer. We invite high-quality submissions that explore the theoretical foundations, practical applications, and innovative uses of AI and machine learning in enhancing the physical layer of communication systems. Contributions on various aspects of native intelligence at the physical layer are welcome, including but not limited to:
Manuscripts must be typed in English and submitted electronically in Microsoft Word (or compatible) format. The word length is approximately 4000 to 8000, and no more than 10 figures and tables should be included. Authors are requested to submit mathematical material and graphics in an editable format.
Please submit your paper through the online submission system of the journal (https://mc03.manuscriptcentral.com/ztecom). Creating an account is necessary for submission.
