  It is anticipated that 6G wireless networks will accelerate the convergence
of the physical and cyber worlds and enable a paradigm-shift in the way we
deploy and exploit communication networks. Machine learning, in particular deep
learning (DL), is expected to be one of the key technological enablers of 6G by
offering a new paradigm for the design and optimization of networks with a high
level of intelligence. In this article, we introduce an emerging DL
architecture, known as the transformer, and discuss its potential impact on 6G
network design. We first discuss the differences between the transformer and
classical DL architectures, and emphasize the transformer's self-attention
mechanism and strong representation capabilities, which make it particularly
appealing for tackling various challenges in wireless network design.
Specifically, we propose transformer-based solutions for various massive
multiple-input multiple-output (MIMO) and semantic communication problems, and
show their superiority compared to other architectures. Finally, we discuss key
challenges and open issues in transformer-based solutions, and identify future
research directions for their deployment in intelligent 6G networks.
