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

Abstract

6G wireless networks are foreseen to speed up the convergence of the physical and cyber worlds and to enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is going 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 in tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks. Finally, we discuss key challenges and open issues in transformerbased solutions, and identify future research directions for their deployment in intelligent 6G networks.

AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications

“…In 2017, the authors of [18] introduced a pure-attention-based DNN architecture, dubbed "transformer", to replace the prevalent RNN models for machine translation tasks. Since then, pure-attention-based DNN models are getting more and more popular in the DL community, while also finding applications in wireless communications [27].…”

“…The update rule of ( 27) is derived for the basic optimizer that performs mini-batch gradient descent. In this case, distributed learning in (28) is equivalent to (27). On the other hand, advanced optimizers, such as Adam or AdaMax [31], often take the momentum of gradients and adaptive learning rates into account.…”