Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

TL;DR

The work investigates integrating semantic communications with Artificial Intelligence Generated Content (AIGC) by adding a generation level to the SemCom stack, enabling receiver-side content creation. It proposes a generation-aware framework with effective information creation and generation components, including PFMs, prompt engineering, and generation inference, to optimize how semantic information drives content generation. A Case study using a Deep Q-Network demonstrates feasible joint optimization of semantic extraction, evaluation metrics, and multi-service resource allocation, revealing convergence within about 50 training episodes. The approach supports personalized, privacy-preserving, and bandwidth-efficient AIGC services suitable for edge and wireless network deployments, with potential extensions to universal metrics and lightweight models.

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.

Figures (5)

• 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.
• 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 Generation inference. On the other hand, edge device 3 applied this framework in VR to generate immersive environments and to synchronize with metaverse server, addressing VR bounding issues by automatically adapting to the user's surroundings environments.
• Figure 3: Semantic variation and downscaling impact in generative task. The figure illustrates ControlNet's structure and where downscaling in semantic information could be deployed. Different extraction models show varied scaling levels, reducing transmitted data. Each method exhibits different sensitivity to downscaling, highlighting the need for flexible resource allocation. Tailoring the downscale factor to the specific model ensures efficient communication by prioritizing less sensitive to more sensitive methods.
• Figure 4: Workflow for defining predictable pairs of semantic extraction methods and evaluation metrics. Service 2 presents a traditional case where a service provider defines both the extraction method and evaluation metric. Services 1 and 3 illustrate the process of selecting the most suitable semantic extraction method when the service provider defines the evaluation metrics. Conversely, Service 4 demonstrates the process of choosing the most appropriate evaluation metrics when the semantic extraction method is defined. This iterative workflow ensures the identification of most predictable combinations for effective semantic extraction and evaluation in the given context.
• Figure 5: Rewards and down-sampling losses over episodes.