GenSC-6G: A Prototype Testbed for Integrated Generative AI, Quantum, and Semantic Communication

2025-01-17

AI Summary

Current 6G semantic communication research suffers from insufficient synergy among generative AI, quantum computing, and semantic communication paradigms. Method: This work introduces GenSC-6G, a prototype testbed that (i) establishes the first unified mathematical model integrating all three paradigms; (ii) proposes a noise-robust synthetic semantic data generation framework supporting quantum noise simulation and semantic information encoding; and (iii) implements a plug-and-play modular architecture incorporating lightweight edge inference and goal-oriented decoders—enabling classification, object localization, semantic super-resolution, and edge-based language reasoning. Contribution/Results: We release a highly scalable, cross-model-compatible semantic dataset augmented with realistic noise profiles. The platform significantly improves development efficiency and robustness of 6G semantic communication systems, providing a reproducible experimental foundation for semantics-driven intelligent networking.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Quantum Machine Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

We introduce a prototyping testbed, GenSC-6G, developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth-generation (6G) applications. The GenSC-6G dataset is designed with noise-augmented synthetic data optimized for semantic decoding, classification, and localization tasks, significantly enhancing flexibility for diverse AI-driven communication applications. This adaptable prototype supports seamless modifications across baseline models, communication modules, and goal-oriented decoders. Case studies demonstrate its application in lightweight classification, semantic upsampling, and edge-based language inference under noise conditions. The GenSC-6G dataset serves as a scalable and robust resource for developing goal-oriented communication systems tailored to the growing demands of 6G networks.

Problem

• 6G Technology
• Generative AI
• Quantum Communication

Innovation

• Generative AI
• Quantum Technology
• Semantic Communication