Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models

核心概念

Generative semantic communication framework with pre-trained models enables ultra-low-rate, low-latency, and channel-adaptive communications.

要約

The content introduces a latency-aware generative semantic communication framework utilizing pre-trained generative models. It focuses on extracting semantic content, multi-modal decomposition, adaptive modulation, and power allocation for efficient communication. The framework aims to achieve ultra-low-rate, low-latency, and channel-adaptive semantic communications. Key highlights include:

• Introduction to Generative AI models for semantic communication.
• Development of a latency-aware semantic communication framework.
• Multi-modal semantic decomposition and synthesis at the transmitter.
• Semantic-aware multi-stream transmission with adaptive modulation.
• Latency-aware adaptive semantic communication scheme.
• Simulation results demonstrating the effectiveness of the proposed framework.
• Evaluation of semantic quality metrics and modulation adaptation guidelines.

統計

"Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications."
"The prompt BER becomes the bottleneck for single-stream transmission, and hence, the BER is kept at 10^-7 to ensure reliable reception of the prompt."

引用

"The recent advent of powerful Generative Foundation Models provides ample opportunities to develop ultra-low-rate semantic communication systems."
"Simulation results showcase the efficacy of the proposed framework in achieving ultra-low-rate, low-latency, and channel-adaptive semantic communications."

抽出されたキーインサイト

Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models

by Li Qiao,Mahd... 場所 arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17256.pdf

深掘り質問

How can the proposed framework impact the future of wireless communication systems?

The proposed framework for latency-aware generative semantic communications with pre-trained diffusion models has the potential to revolutionize wireless communication systems. By leveraging generative AI models and semantic communication techniques, this framework enables ultra-low-rate, low-latency, and channel-adaptive communication. This impact can lead to significant advancements in wireless networks by allowing for efficient transmission of semantic content at extremely low data rates. The use of pre-trained models enhances the universality and compatibility of the system, making it applicable to various datasets and tasks. Overall, this framework can pave the way for more efficient and effective communication systems in the future.

What are the potential drawbacks or limitations of relying on pre-trained generative models for semantic communication?

While pre-trained generative models offer numerous benefits for semantic communication, there are also potential drawbacks and limitations to consider. One limitation is the need for extensive training data to ensure the models' effectiveness across various tasks and datasets. Additionally, pre-trained models may not always capture the specific nuances or context of a particular communication task, leading to potential inaccuracies or misinterpretations. Another drawback is the computational complexity and resource requirements associated with training and deploying these models, which can be a barrier for some applications. Furthermore, pre-trained models may struggle with adapting to real-time or dynamic communication scenarios, limiting their flexibility in certain contexts.

How can the concept of semantic communication be applied to other fields beyond wireless networks?

The concept of semantic communication, as demonstrated in the context of wireless networks, can be applied to various other fields to enhance communication and information exchange. In healthcare, semantic communication can improve the accuracy and efficiency of medical data sharing between healthcare providers and systems. In autonomous vehicles, semantic communication can enable vehicles to exchange critical information about road conditions, traffic patterns, and potential hazards. In education, semantic communication can facilitate personalized learning experiences by tailoring content delivery based on individual student needs and preferences. Overall, the application of semantic communication principles can revolutionize communication and information exchange in diverse fields beyond wireless networks.