Deadline-Aware Bandwidth Allocation for Semantic Generative Communication with Diffusion Models

TL;DR

This paper introduces a bandwidth allocation scheme for semantic generative communication in AI-driven image inpainting, optimizing bandwidth use by considering semantic deadlines to improve image quality.

Contribution

It proposes a novel bandwidth allocation method based on semantic deadlines, enhancing efficiency and performance in semantic generative communication systems.

Findings

The scheme achieves higher PSNR for a given bandwidth.

It effectively allocates bandwidth respecting semantic deadlines.

Experimental results validate improved image quality.

Abstract

The importance of Radio Access Network (RAN) in support Artificial Intelligence (AI) application services has grown significantly, underscoring the need for an integrated approach that considers not only network efficiency but also AI performance. In this paper we focus on a semantic generative communication (SGC) framework for image inpainting application. Specifically, the transmitter sends semantic information, i.e., semantic masks and textual descriptions, while the receiver utilizes a conditional diffusion model on a base image, using them as conditioning data to produce the intended image. In this framework, we propose a bandwidth allocation scheme designed to maximize bandwidth efficiency while ensuring generation performance. This approach is based on our finding of a Semantic Deadline--the minimum time that conditioning data is required to be injected to meet a given performance threshold--within the multi-modal SGC framework. Given this observation, the proposed scheme allocates limited bandwidth so that each semantic information can be transmitted within the corresponding semantic deadline. Experimental results corroborate that the proposed bandwidth allocation scheme achieves higher generation performance in terms of PSNR for a given bandwidth compared to traditional schemes that do not account for semantic deadlines.