Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation

TL;DR

This paper introduces a generative semantic communication framework using foundation models, analyzing perception-error relationships and proposing semantic-aware power allocation to enhance low-rate, high-fidelity data transmission.

Contribution

It develops a novel generative SemCom framework with perceptual analysis and introduces semantic-aware power allocation methods for efficient, high-quality semantic data transmission.

Findings

Perception-error relationship is non-decreasing with transmission reliability.

Semantic-aware power allocation reduces power consumption by up to 90%.

Proposed methods outperform conventional approaches in image SemCom tasks.

Abstract

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems allowing high fidelity exchange of semantic information at ultra low rates. In this work, a generative SemCom framework with pretrained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. To characterize the impact of transmission reliability on the perceptual quality of the regenerated signal, their mathematical relationship is analyzed from a rate-distortion-perception perspective, which is proved to be non-decreasing. The semantic values are defined to measure the semantic information of multimodal semantic features accordingly. We also investigate semantic-aware power allocation problems aiming at power consumption minimization for ultra low rate and high fidelity SemComs. To solve these problems, two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Numerically, perception-error functions and semantic values of semantic data streams under both schemes for image tasks are obtained based on the Kodak dataset. Simulation results show that our proposed semanticaware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).