Information Theory and AI-Driven Communications

Dear Colleagues,

Whilst information theory (IT)-driven communications have led the evolution of mobile communication systems in the recent decades, artificial intelligence (AI)-driven approaches have been demonstrating great potentials in bolstering the future (r)evolutions. However, there is still a lack of theoretical understanding to underpin such a revolutionary thread of integrated IT-/AI-driven communications. This Special Issue aims to present cutting-edge research on (1) transformations of IT-driven communication frameworks and algorithms to AI-driven architectures and models using e.g., learning-to-code, learning-to-optimize, and learning-to-unfold approaches; and (2) theoretical foundations of AI-driven models and algorithms for communications with respect to generalization, robustness, explanation, security and privacy from information and learning theoretical perspectives.

In particular, we welcome contributions that investigate innovative model/data-driven deep learning approaches to digital communications for source/channel coding/decoding, multiuser multi-input multi-output (MIMO) detection, channel estimation/prediction/feedback, multiuser/multicell/cell-free MIMO precoding, interference management, radio resource management, etc. Meanwhile, we encourage studying theoretical guarantees of AI models/algorithms with respect to in-distribution and out-of-distribution generalization across different wireless environments, robustness against network dynamics, channel uncertainties, adversarial attacks, and the wide range of explainability, security, and privacy. Prospective authors are invited to submit original manuscripts on the following topics including, but not limited to, the following:

• Theoretical foundations of AI-driven communications.
• AI-driven transceiver design for multiuser communication systems.
• AI-driven interference management in multicell/cell-free MIMO networks.
• AI-driven radio resource management in wireless networks.
• Generalization analysis of AI-driven communication algorithms.
• Robustness analysis of AI-driven communications algorithms.
• Explainability studies of AI-driven communication algorithms.
• Security and privacy aspects of AI-driven communications.
• The interplay between IT- and AI-driven communications.

We look forward to receiving your contributions.

Prof. Dr. Xinping Yi
Dr. Abdellatif Zaidi
Guest Editors

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