Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation

TL;DR

This paper presents a flexible, multi-task semantic communication framework that adapts to channel conditions using a masked auto-encoder architecture, optimizing meaningful data transmission for diverse tasks.

Contribution

It introduces a novel channel-adaptive, multi-task-aware framework that dynamically prioritizes and transmits semantically significant information based on real-time conditions.

Findings

Outperforms conventional methods in image reconstruction and object detection.

Demonstrates robustness across heterogeneous channel environments.

Ensures minimal performance loss under resource constraints.

Abstract

The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has driven the development of robust, resource-efficient frameworks. This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture. Our framework optimizes the transmission of meaningful information by incorporating a multi-task-aware scoring mechanism that identifies and prioritizes semantically significant data across multiple concurrent tasks. A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions. By jointly optimizing semantic relevance and transmission efficiency, the framework ensures minimal performance degradation under resource constraints. Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection. These results underscore the framework's adaptability to heterogeneous channel environments and its scalability for multi-task applications, positioning it as a promising solution for next-generation semantic communication networks.