Diffusion-based Generative Multicasting with Intent-aware Semantic Decomposition

2024-11-04

arXiv.org

AI Summary

To address low-latency and heterogeneous semantic requirements in multi-user semantic communications for future wireless networks, this paper proposes an intent-aware generative semantic multicast framework. The transmitter decomposes the source signal according to each user’s semantic intent, transmitting only the intended semantic classes while broadcasting a lightweight shared semantic graph; users then collaboratively reconstruct non-intended classes locally using pre-trained diffusion models. This work pioneers the integration of generative diffusion models (GDMs) into semantic multicast, enabling intent-driven semantic decomposition and generative reconstruction. We further design a communication-computation co-optimized, per-class adaptive parameter allocation mechanism that jointly optimizes transmit power, coding rate, and model inference overhead. Experimental results demonstrate that, compared to conventional non-generative and intent-agnostic baselines, the proposed framework significantly reduces end-to-end latency, improves spectral efficiency, and enhances privacy protection for non-intended semantic content.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal to multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. The transmitter then sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, i.e. non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. This improves utilization of the wireless resources, with better preserving privacy of the non-intended classes. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users.

Problem

• Minimizing latency in intent-based semantic multicasting with diffusion models
• Optimizing communication parameters for partial reconstruction and synthesis
• Reducing wireless resource usage while maintaining perceptual quality

Innovation

• Intent-aware semantic multicasting with diffusion models
• Decomposes signals into semantic classes for transmission
• Adapts communication parameters to minimize latency