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

TL;DR

This paper introduces a latency-aware semantic communication framework utilizing pre-trained generative diffusion models, enabling low-latency, high-quality signal synthesis at extremely low data rates in wireless networks.

Contribution

It proposes a novel semantic communication scheme that decomposes signals into multiple modalities, employs adaptive coding, and leverages pre-trained generative models for high-fidelity reconstruction.

Findings

Achieves ultra-low-rate, low-latency communication

Demonstrates robustness to wireless channel variations

Provides high perceptual quality in signal synthesis

Abstract

Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications at extremely low data rates in future wireless networks. In this paper, we develop a latency-aware semantic communications framework with pre-trained generative models. The transmitter performs multi-modal semantic decomposition on the input signal and transmits each semantic stream with the appropriate coding and communication schemes based on the intent. For the prompt, we adopt a re-transmission-based scheme to ensure reliable transmission, and for the other semantic modalities we use an adaptive modulation/coding scheme to achieve robustness to the changing wireless channel. Furthermore, we design a semantic and latency-aware scheme to allocate transmission power to different semantic modalities based on their importance subjected to semantic quality constraints. At the receiver, a pre-trained generative model synthesizes a high fidelity signal using the received multi-stream semantics. Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications.