Semantic Communication-Empowered AIGC Provisioning Framework
Welcome to the Semantic Communication-Empowered AIGC (AI-Generated Content) Provisioning Framework. This repository presents an advanced framework for optimizing AI-generated content transmission over semantic communication channels. The solution is designed to enable efficient workload management and flexible transmission strategies, integrating state-of-the-art techniques like Deep Q-learning and diffusion-based transceivers.
Features The project includes two major components:
D3QN-Based Workload Tradeoff Scheme:
A Deep Double Q-Network (D3QN) based optimization mechanism that handles workload distribution across different devices or nodes. It ensures efficient trade-offs between computational workload, energy consumption, and transmission delay for AI-generated content (AIGC).
Diffusion-Based Workload Adjustable Transceiver Design:
A diffusion model that adapts the communication transceiver’s workload based on real-time channel conditions and content complexity. This design facilitates flexible and efficient transmission while maintaining high content fidelity. Repository Structure rust
Getting Started Prerequisites Python 3.7 or higher
Required libraries are listed in the requirements.txt file. You can install them by running:
bash pip install -r requirements.txt Running the Project Colab Preview Version:
A preview version of the project is available as a Google Colab notebook. This version allows users to explore the basic functionality and concepts of the transceiver of the SemAIGC framework. Link to the Colab Notebook
Offline Version (Coming Soon):
The offline version, which will include optimized code and enhancements for local execution, is currently in development and will be added soon.
Usage
D3QN Workload Tradeoff: Go to the D3QN_Workload_Tradeoff directory to find the relevant scripts for training and testing the D3QN model. Adjust the hyperparameters and workload configurations as per your needs.
Diffusion Transceiver Design: Explore the Diffusion_Transceiver_Design folder for the diffusion-based transceiver design. Modify the diffusion model settings to experiment with different communication scenarios and workloads.
Thank you for checking out our project! Stay tuned for upcoming updates.