Generative Semantic Communication: Architectures, Technologies, and Applications

Abstract

Semantic communication (SemCom) has emerged as a transformative paradigm for future wireless networks, aiming to improve communication efficiency by transmitting only the semantic meaning (or its encoded version) of the source data rather than the complete set of bits (symbols). However, traditional deep learning-based SemCom systems present challenges such as limited generalization, low robustness, and inadequate reasoning capabilities, primarily due to the inherently discriminative nature of deep neural networks. To address these limitations, generative artificial intelligence (GAI) is seen as a promising solution, offering notable advantages in learning complex data distributions, transforming data between high- and low-dimensional spaces, and generating high-quality content. This paper explores the applications of GAI in SemCom and presents a comprehensive study. It begins by introducing three widely used SemCom systems enabled by classical GAI models: variational autoencoders, generative adversarial networks, and diffusion models. For each system, the fundamental concept of the GAI model, the corresponding SemCom architecture, and a literature review of recent developments are provided. Subsequently, a novel generative SemCom system is proposed, incorporating cutting-edge GAI technology—large language models (LLMs). This system features LLM-based artificial intelligence (AI) agents at both the transmitter and receiver, which act as “brains” to enable advanced information understanding and content regeneration capabilities, respectively. Unlike traditional systems that focus on bitstream recovery, this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter. As a result, the communication paradigm shifts from “information recovery” to “information regeneration,” marking a new era in generative SemCom. A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system, showing a 99.98% reduction in communication overhead and a 53% improvement in average retrieval accuracy compared to traditional communication systems. Furthermore, four typical application scenarios for generative SemCom are described, followed by a discussion of three open issues for future research. In summary, this paper provides a comprehensive set of guidelines for applying GAI in SemCom, laying the groundwork for the efficient deployment of generative SemCom in future wireless networks.

Topics & Concepts