Generative Artificial Intelligence Assisted Multi-modal Semantic Extraction for NOMA-based Image Transmissions

TL;DR

This paper proposes a GAI-assisted semantic communication framework for NOMA-based image transmission, optimizing semantic feature selection and transmission control to improve recovery accuracy and latency.

Contribution

It introduces a novel joint optimization framework combining semantic feature selection and transmission control using an IM-PPO method for NOMA-based image transmission.

Findings

Enhanced semantic recovery accuracy and reduced transmission latency.

IM-PPO improves learning efficiency over benchmark methods.

Joint optimization significantly benefits NOMA-based semantic image transmission.

Abstract

In this paper, we investigate a generative artificial intelligence (GAI)-assisted semantic communication framework for non-orthogonal multiple access (NOMA)-based image transmissions. Semantic users (SUs) extract cross-modal semantic features from the raw images, which are then used for image recovery by leveraging a GAI model. The GAI enhances the generalization and recovery of semantic image transmissions, while NOMA efficiently allocates transmission capacities to SUs based on their traffic demands. Thus, the semantic extraction and transmission control jointly affect both semantic recovery performance and transmission overhead. We maximize a weighted performance of transmission latency and semantic recovery accuracy by jointly optimizing the semantic feature selection at the semantic level, as well as the receive beamforming and NOMA decoding order at the transmission level. To reduce potential redundancy in semantic features and improve optimization efficiency, we develop an importance-aware and model-driven proximal policy optimization (IM-PPO) framework. Specifically, we quantify and retain high-importance semantic features to enhance the learning efficiency of PPO, while model-based optimization methods are used to adapt the transmission control variables. Numerical results validate that the joint adjustment of the semantic feature selection and the transmission control significantly improves the semantic recovery accuracy and the transmission latency performance. Moreover, the IM-PPO framework effectively leverages the model information to improve the learning efficiency compared to benchmark methods.