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

• URL: http://arxiv.org/abs/2205.03770v1
• Date: Sun, 8 May 2022 03:22:20 GMT
• Title: Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
• Authors: Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen, Deniz G\"und\"uz, H. Vincent Poor
• Abstract summary: We introduce an emerging deep learning architecture, known as the transformer, and discuss its potential impact on 6G network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks.
• Score: 71.21459460829409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 transformer-based solutions, and identify future research directions for their deployment in intelligent 6G networks.

Related papers

• AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems [3.5370806221677245]
  This paper explores the seamless integration of Large Language Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems. LLMs and GPTs will collaboratively take center stage alongside traditional pre-generative AI and machine learning (ML) algorithms.
  arXiv  Detail & Related papers  (2023-11-10T02:59:16Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• Holistic Network Virtualization and Pervasive Network Intelligence for 6G [14.35331138476144]
  We look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI) We aim to inspire further discussions and developments on the potential architecture of 6G.
  arXiv  Detail & Related papers  (2023-01-02T04:15:33Z)
• AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks [7.318997639507269]
  We propose a novel layer-based HetNet architecture which distributes tasks associated with different machine learning approaches across network layers and entities. Such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency.
  arXiv  Detail & Related papers  (2022-06-04T22:03:19Z)
• Transformers in Vision: A Survey [101.07348618962111]
  Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence. Transformers require minimal inductive biases for their design and are naturally suited as set-functions. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline.
  arXiv  Detail & Related papers  (2021-01-04T18:57:24Z)
• A Survey on Visual Transformer [126.56860258176324]
  Transformer is a type of deep neural network mainly based on the self-attention mechanism. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages.
  arXiv  Detail & Related papers  (2020-12-23T09:37:54Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• Federated Learning for 6G Communications: Challenges, Methods, and Future Directions [71.31783903289273]
  We introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.
  arXiv  Detail & Related papers  (2020-06-04T15:17:19Z)
• Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding [17.186326961526994]
  We present the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning approaches. This article motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks.
  arXiv  Detail & Related papers  (2020-04-22T17:20:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.