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Generative AI for Semantic Communication: Architecture, Challenges, and Outlook

Le Xia, Yao Sun, Chengsi Liang, Lei Zhang, Muhammad Ali Imran, Dusit Niyato

TL;DR

A novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design that enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization.

Abstract

Semantic communication (SemCom) is expected to be a core paradigm in future communication networks, yielding significant benefits in terms of spectrum resource saving and information interaction efficiency. However, the existing SemCom structure is limited by the lack of context-reasoning ability and background knowledge provisioning, which, therefore, motivates us to seek the potential of incorporating generative artificial intelligence (GAI) technologies with SemCom. Recognizing GAI's powerful capability in automating and creating valuable, diverse, and personalized multimodal content, this article first highlights the principal characteristics of the combination of GAI and SemCom along with their pertinent benefits and challenges. To tackle these challenges, we further propose a novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design. Specifically, by employing global and local GAI models, our GAI-SCN enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization. Afterward, we present a detailed implementation workflow of GAI-SCN, followed by corresponding initial simulations for performance evaluation in comparison with two benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of GAI-SCN.

Generative AI for Semantic Communication: Architecture, Challenges, and Outlook

TL;DR

A novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design that enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization.

Abstract

Semantic communication (SemCom) is expected to be a core paradigm in future communication networks, yielding significant benefits in terms of spectrum resource saving and information interaction efficiency. However, the existing SemCom structure is limited by the lack of context-reasoning ability and background knowledge provisioning, which, therefore, motivates us to seek the potential of incorporating generative artificial intelligence (GAI) technologies with SemCom. Recognizing GAI's powerful capability in automating and creating valuable, diverse, and personalized multimodal content, this article first highlights the principal characteristics of the combination of GAI and SemCom along with their pertinent benefits and challenges. To tackle these challenges, we further propose a novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design. Specifically, by employing global and local GAI models, our GAI-SCN enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization. Afterward, we present a detailed implementation workflow of GAI-SCN, followed by corresponding initial simulations for performance evaluation in comparison with two benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of GAI-SCN.
Paper Structure (12 sections, 5 figures, 1 table)

This paper contains 12 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. When SemCom Meets GAI
  3. SemCom Systems
  4. Typical GAI Technologies
  5. Interplay between SemCom and GAI
  6. GAI-Integrated SemCom Network Framework
  7. Hierarchical Structure of GAI-SCN
  8. Implementation Workflow
  9. Case Study: Image Transmission Service Provisioning in GAI-SCN
  10. Open Research Issues and Outlooks
  11. Conclusions
  12. Acknowledgments

Figures (5)

  • Figure 1: Overview of SemCom systems and four types of typical GAI technologies along with three aspects of interplay between SemCom and GAI.
  • Figure 2: Comparisons among three different approaches of GAI-SemCom, SemCom, and traditional communication in terms of their pros and cons.
  • Figure 3: Illustration of the proposed GAI-SCN framework in a collaborative cloud-edge-mobile design, where an exemplification of image service provisioning is presented.
  • Figure 4: A schematic diagram of implementing a complete round of semantic service provisioning in the GAI-SCN, including three successive stages of Initial Network Preparation, Generative AI-integrated SemCom Service Provisioning, and Model Synchronization and Update.
  • Figure 5: Comparisons between original and recovered images by the proposed GAI-SCN framework in terms of three metrics: A) Semantic similarity by spaCy; B) Object quantity discrepancy; C) Recovery ratio of original objects.