mlsanityData to 14 Oct 2026

Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks

arXiv 2407.20970 ↗First posted 30 Jul 2024cs.NI cs.AIcited by 7

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Abstract (arXiv)

With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs can be used under the umbrella of semantic communication at the network edge for efficient communication in IoT networks. Finally, we discuss a few applications and analyze the challenges and opportunities to develop such systems.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 30 Jul 2024.

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7 of 12 references.

References · 7

  1. 2020Deep Learning Enabled Semantic Communication Systemscited by 292
  2. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 175
  3. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  4. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  5. 2023Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generationcited by 17
  6. 2023Semantic Communications Based on Adaptive Generative Models and Information Bottleneckcited by 16
  7. 2024Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Taskscited by 9

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Citing papers per year: 2024: 2; 2025: 5.

7 papers.