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

• URL: http://arxiv.org/abs/2308.04942v2
• Date: Sat, 20 Jan 2024 05:27:36 GMT
• Title: Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation
• Authors: Guangyuan Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, and Xuemin (Sherman) Shen
• Abstract summary: This paper develops a conceptual model for the integration of AIGC and SemCom. A novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information. The framework can adapt to different types of content generated, the required quality, and the semantic information utilized.
• Score: 75.73229320559996
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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