Importance-Aware Source-Channel Coding for Multi-Modal Task-Oriented Semantic Communication

TL;DR

This paper introduces importance-aware source-channel coding strategies for multi-modal semantic communication, leveraging generative AI to prioritize critical information and improve efficiency in task-oriented systems.

Contribution

It proposes dynamic importance-aware coding methods and rate-splitting transmission to enhance resource utilization and robustness in multi-user semantic communication.

Findings

Prioritized essential information improves transmission fidelity.

Adaptive coding strategies optimize resource use based on importance levels.

Rate-splitting enhances robustness in multicast scenarios.

Abstract

This paper explores the concept of information importance in multi-modal task-oriented semantic communication systems, emphasizing the need for high accuracy and efficiency to fulfill task-specific objectives. At the transmitter, generative AI (GenAI) is employed to partition visual data objects into semantic segments, each representing distinct, task-relevant information. These segments are subsequently encoded into tokens, enabling precise and adaptive transmission control. Building on this frame work, we present importance-aware source and channel coding strategies that dynamically adjust to varying levels of significance at the segment, token, and bit levels. The proposed strategies prioritize high fidelity for essential information while permitting controlled distortion for less critical elements, optimizing overall resource utilization. Furthermore, we address the source-channel coding challenge in semantic multiuser systems, particularly in multicast scenarios, where segment importance varies among receivers. To tackle these challenges, we propose solutions such as rate-splitting coded progressive transmission, ensuring flexibility and robustness in task-specific semantic communication.