# Generative AI for Semantic Communication: Architecture, Challenges, and Outlook

arXiv: https://arxiv.org/abs/2308.15483 · HTML: https://mlsanity.com/paper/2308.15483/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2023.

## Built on by (most cited first, 20 of 20)

- [Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2403.17256/) (2024)
- [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication](https://mlsanity.com/paper/2310.17705/) (2023)
- [Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Models](https://mlsanity.com/paper/2411.02334/) (2024)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [Retrieval-augmented Generation for GenAI-enabled Semantic Communications](https://mlsanity.com/paper/2412.19494/) (2024)
- [Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search](https://mlsanity.com/paper/2506.19451/) (2025)
- [Video TokenCom: Textual Intent-Guided Multi-Rate Video Token Communications with UEP-Based Adaptive Source-Channel Coding](https://mlsanity.com/paper/2603.02470/) (2026)
- [Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications](https://mlsanity.com/paper/2602.12338/) (2026)
- [A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0](https://mlsanity.com/paper/2311.13731/) (2023)
- [Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images](https://mlsanity.com/paper/2506.19893/) (2025)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)
- [Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy?](https://mlsanity.com/paper/2407.02292/) (2024)
- [Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks](https://mlsanity.com/paper/2506.12786/) (2025)
- [Large AI Model-Enabled Generative Semantic Communications for Image Transmission](https://mlsanity.com/paper/2509.21394/) (2025)
- [Semantic-Driven AI Agent Communications: Challenges and Solutions](https://mlsanity.com/paper/2510.00381/) (2025)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

Semantic communication (SemCom) is expected to be a core paradigm in future communication networks, yielding significant benefits in terms of spectrum resource saving and information interaction efficiency. However, the existing SemCom structure is limited by the lack of context-reasoning ability and background knowledge provisioning, which, therefore, motivates us to seek the potential of incorporating generative artificial intelligence (GAI) technologies with SemCom. Recognizing GAI's powerful capability in automating and creating valuable, diverse, and personalized multimodal content, this article first highlights the principal characteristics of the combination of GAI and SemCom along with their pertinent benefits and challenges. To tackle these challenges, we further propose a novel GAI-integrated SemCom network (GAI-SCN) framework in a cloud-edge-mobile design. Specifically, by employing global and local GAI models, our GAI-SCN enables multimodal semantic content provisioning, semantic-level joint-source-channel coding, and AIGC acquisition to maximize the efficiency and reliability of semantic reasoning and resource utilization. Afterward, we present a detailed implementation workflow of GAI-SCN, followed by corresponding initial simulations for performance evaluation in comparison with two benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of GAI-SCN.