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A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

Runze Cheng, Yao Sun, Dusit Niyato, Lan Zhang, Lei Zhang, Muhammad Ali Imran

TL;DR

A semantic communication-empowered AIGC generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom.

Abstract

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To this end, this paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, a Resource-aware wOrklOad Trade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

TL;DR

A semantic communication-empowered AIGC generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom.

Abstract

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To this end, this paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, a Resource-aware wOrklOad Trade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.
Paper Structure (20 sections, 2 theorems, 29 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 20 sections, 2 theorems, 29 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background: AIGC in Wireless
  3. Motivation
  4. Contributions and Organization
  5. SemCom-Empowered AIGC Generation and Transmission Framework
  6. Cloud Pre-Training
  7. Edge Encoding
  8. Local Decoding
  9. Workload-Adjustable Transceiver Design
  10. Encoder Design at Edge Transmitter
  11. Decoder Design at Local Receiver
  12. Training Algorithm
  13. Intelligent Workload Trade-off Scheme Design
  14. T2I Service Evaluation Metrics
  15. Computing Workload Adaptation Problem Formulation
  16. ...and 5 more sections

Key Result

Proposition 1

Assume semantic noise$\bm{\epsilon}^{\cal C} \sim {\cal N}(0,\sigma^{2}\bm{I})$ is gradually added to semantic information, the reverse diffusion process from denoising step $t$ to $t-1$ in the fine-tuning module is is a set of time related constants, and $\bm{\epsilon}_{\bm{\theta}}(\mathbf{z}_{t},t,\mathbf{z}_{ t\!e\!x\!t})$ is the noise predicted from semantic information $\mathbf{z}_{t}$. Add

Figures (9)

  • Figure 1: The proposed SemAIGC framework. Stage I is the cloud pre-training process that carried out in the cloud server to train transceiver semantic processing networks and ROOT scheme networks; Stage II edge encoding is executed by the edge transmitter, for example, a base station with an edge server, which is the major stage of semantic information generation and transmission; Stage III is the local decoding stage in the local receiver, deployed to fine-tune the semantic noise caused by channel noise and restructure semantic information into images.
  • Figure 2: The workload-adjustable transceiver in SemAIGC.
  • Figure 3: The diffusion and reverse diffusion processes in the semantic generation module.
  • Figure 4: The delivered images of three AIGC generation and transmission frameworks under different SNRs (text input: "A cute furry cat", the seed value for initial noise: 30).
  • Figure 5: The image details of three AIGC generation and transmission frameworks under 0dB wireless channel.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2