TY - JOUR
T1 - Transformer-Empowered 6G Intelligent Networks
T2 - From Massive MIMO Processing to Semantic Communication
AU - Wang, Yang
AU - Gao, Zhen
AU - Zheng, Dezhi
AU - Chen, Sheng
AU - Gunduz, Deniz
AU - Poor, H. Vincent
N1 - Publisher Copyright:
IEEE
PY - 2022
Y1 - 2022
N2 - 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 transformerbased solutions, and identify future research directions for their deployment in intelligent 6G networks.
AB - 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 transformerbased solutions, and identify future research directions for their deployment in intelligent 6G networks.
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U2 - 10.1109/MWC.008.2200157
DO - 10.1109/MWC.008.2200157
M3 - Article
AN - SCOPUS:85141988327
SN - 1536-1284
SP - 1
EP - 9
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
ER -