[Paper Review] Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

Guangyuan Liu, Hongyang Du+6 more|arXiv (Cornell University)|Aug 9, 2023

Cognitive Computing and Networks4 citations

TL;DR

This paper proposes a novel semantic communication framework integrating Artificial Intelligence Generated Content (AIGC) with semantic communication (SemCom), introducing a content generation level atop the semantic level to enable efficient, context-aware content creation. By using AIGC as an encoder/decoder and optimizing resource allocation via Deep Q-Network (DQN), the framework achieves convergence in ~50 epochs with minimal loss, demonstrating feasibility and adaptability for diverse AIGC workloads under bandwidth and quality constraints.

ABSTRACT

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality, and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics.

Motivation & Objective

• To address the challenge of efficient, context-aware content creation in AIGC by integrating semantic communication (SemCom) to improve bandwidth, privacy, and energy efficiency.
• To develop a unified conceptual model that introduces a content generation level on top of the semantic level, clarifying the interaction between AIGC and SemCom.
• To design a joint optimization framework for semantic extraction and evaluation metrics tailored to AIGC services, ensuring adaptability across content types and quality requirements.
• To validate the feasibility of dynamic resource allocation in AIGC-SemCom systems using reinforcement learning (DQN) and assess convergence and performance under varying downscaling factors.

Proposed method

• Proposes a novel conceptual model with a content generation level above the semantic level to structure AIGC-SemCom interaction.
• Employs AIGC models as both encoder and decoder for semantic information, enabling end-to-end semantic-aware content generation and reconstruction.
• Introduces a joint optimization framework that coordinates semantic extraction and quality evaluation metrics specific to AIGC output.
• Utilizes Deep Q-Network (DQN) to solve the dynamic resource allocation problem, with reward function inversely related to image generation loss.
• Models resource allocation as a Markov Decision Process (MDP), where state includes input resolution and compression level, and action is the allocation policy.
• Employs a loss function based on the difference between original and generated image quality, guiding DQN to minimize degradation during transmission.

Figure 1: SemCom-enabled AIGC model, which is divided into physical level and semantic level for SemCom to support generation level and effective information creation level.

Experimental results

Research questions

• RQ1 How can AIGC and semantic communication be conceptually integrated to enable efficient, context-aware content creation?
• RQ2 What is the optimal way to jointly optimize semantic extraction and evaluation metrics for diverse AIGC services?
• RQ3 Can reinforcement learning (DQN) effectively solve the dynamic resource allocation problem in AIGC-SemCom systems?
• RQ4 How does the system perform under varying input conditions such as image resolution and compression levels?
• RQ5 What is the convergence behavior and performance trade-off of the proposed DQN-based optimization framework?

Key findings

• The DQN-based optimization framework converges after approximately 50 training epochs, indicating stable and effective learning of optimal resource allocation policies.
• The system maintains low image generation loss despite increasing downscaling factors, demonstrating robustness to semantic degradation during transmission.
• The reward function, inversely modeled on loss, successfully guides the DQN to prioritize resource allocation that preserves image quality.
• The framework effectively manages resource allocation across four distinct AIGC services, confirming its adaptability to diverse content types and requirements.
• The integration of AIGC with SemCom enables significant improvements in bandwidth efficiency, energy efficiency, and privacy without compromising content quality.
• The results validate the feasibility of the proposed AIGC-SemCom framework for real-world deployment in dynamic, resource-constrained environments.

Figure 2: AIGC model as generation encoder and decoder. In this AIGC framework, edge devices 1 and 2 represent users in a professional meeting. This step fuses their Semantic/prompt representation through Prompt engineering , guiding AI models to generate a unified modern meeting room theme in Gener