Zhijin Qin;Le Liang;Zijing Wang;Shi Jin;Xiaoming Tao;Wen Tong;Geoffrey Ye Li
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AI Empowered Wireless Communications: From Bits to Semantics
Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips and tricks on realizing such performance gains when training AI/ML models. In particular, we discuss in detail the use of AI/ML to revolutionize key physical layer and lower medium access control (MAC) layer functionalities in traditional wireless communication systems. In addition, we provide a comprehensive overview of the AI/ML-enabled semantic communication systems, including key techniques from data generation to transmission. We also investigate the role of AI/ML as an optimization tool to facilitate the design of efficient resource allocation algorithms in wireless communication networks at both bit and semantic levels. Finally, we analyze major challenges and roadblocks in applying AI/ML in practical wireless system design and share our thoughts and insights on potential solutions.
期刊介绍:
Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.