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It is anticipated that 6G wireless networks will accelerate the convergenceof the physical and cyber worlds and enable a paradigm-shift in the way wedeploy and exploit communication networks. Machine learning, in particular deeplearning (DL), is expected to be one of the key technological enablers of 6G byoffering a new paradigm for the design and optimization of networks with a highlevel of intelligence. In this article, we introduce an emerging DLarchitecture, known as the transformer, and discuss its potential impact on 6Gnetwork design. We first discuss the differences between the transformer andclassical DL architectures, and emphasize the transformer's self-attentionmechanism and strong representation capabilities, which make it particularlyappealing for tackling various challenges in wireless network design.Specifically, we propose transformer-based solutions for various massivemultiple-input multiple-output (MIMO) and semantic communication problems, andshow their superiority compared to other architectures. Finally, we discuss keychallenges and open issues in transformer-based solutions, and identify futureresearch directions for their deployment in intelligent 6G networks.

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication