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

• URL: http://arxiv.org/abs/2501.09918v1
• Date: Fri, 17 Jan 2025 02:20:52 GMT
• Title: GenSC-6G: A Prototype Testbed for Integrated Generative AI, Quantum, and Semantic Communication
• Authors: Brian E. Arfeto, Shehbaz Tariq, Uman Khalid, Trung Q. Duong, Hyundong Shin,
• Abstract summary: GenSC-6G dataset is designed with noise-augmented synthetic data optimized for semantic decoding, classification, and localization tasks. This prototype supports seamless modifications across baseline models, communication modules, and goal-oriented decoders.
• Score: 15.241605187543616
• License:
• 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.

Related papers

• Generative AI Enabled Matching for 6G Multiple Access [51.00960374545361]
  We propose a GenAI-enabled matching generation framework to support 6G multiple access. We show that our framework can generate more effective matching strategies based on given conditions and predefined rewards.
  arXiv  Detail & Related papers  (2024-10-29T13:01:26Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Integrating Pre-Trained Language Model with Physical Layer Communications [19.20941153929975]
  We introduce a practical ondevice AI communication framework, integrated with physical layer (PHY) communication functions. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities.
  arXiv  Detail & Related papers  (2024-02-18T17:27:51Z)
• At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence [11.847999494242387]
  Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. We outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy
  arXiv  Detail & Related papers  (2024-02-02T06:23:25Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts [89.04751776308656]
  This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
  arXiv  Detail & Related papers  (2023-09-05T23:24:56Z)
• Enabling the Wireless Metaverse via Semantic Multiverse Communication [82.47169682083806]
  Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems. We propose a novel semantic communication framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs) An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI)
  arXiv  Detail & Related papers  (2022-12-13T21:21:07Z)
• In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks [61.416494781759326]
  In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level. We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
  arXiv  Detail & Related papers  (2022-10-07T13:41:15Z)
• True-data Testbed for 5G/B5G Intelligent Network [46.09035008165811]
  We build the world's first true-data testbed for 5G/B5G intelligent network (TTIN) TTIN comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. This paper elaborates on the system architecture and module design of TTIN.
  arXiv  Detail & Related papers  (2020-11-26T06:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.