Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization

TL;DR

This paper introduces a novel semantic communication framework utilizing generative AI models like GANs and diffusion models for efficient image compression and resource allocation in edge networks, significantly improving bandwidth and latency.

Contribution

It proposes a new semantic-preserving image compression method and a goal-oriented edge network optimization framework integrating generative AI and the Information Bottleneck principle.

Findings

Semantic-aware models outperform traditional compression in quality and efficiency.

The approach reduces bandwidth and latency in edge networks.

Generative models enable high-quality image reconstruction from minimal data.

Abstract

As digital technologies advance, communication networks face challenges in handling the vast data generated by intelligent devices. Autonomous vehicles, smart sensors, and IoT systems necessitate new paradigms. This thesis addresses these challenges by integrating semantic communication and generative models for optimized image compression and edge network resource allocation. Unlike bit-centric systems, semantic communication prioritizes transmitting meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression using Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These models compress images by encoding only semantically relevant features, allowing for high-quality reconstruction with minimal transmission. Additionally, a Goal-Oriented edge network optimization framework is introduced, leveraging the Information Bottleneck principle and stochastic optimization to dynamically allocate resources and enhance efficiency. By integrating semantic communication into edge networks, this approach balances computational efficiency and communication effectiveness, making it suitable for real-time applications. The thesis compares semantic-aware models with conventional image compression techniques using classical and semantic evaluation metrics. Results demonstrate the potential of combining generative AI and semantic communication to create more efficient semantic-goal-oriented communication networks that meet the demands of modern data-driven applications.