Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

TL;DR

This paper introduces a novel deep joint source and channel coding framework for 6G semantic communications that simultaneously supports image recovery and classification, enhancing robustness and efficiency over wireless channels.

Contribution

It proposes a unified end-to-end deep JSCC model for multi-task image recovery and classification, incorporating a gating mechanism for channel adaptability, which is a significant advancement over existing single-task designs.

Findings

Outperforms benchmark schemes in image recovery quality.

Enables direct classification in feature space with high accuracy.

Demonstrates robustness against variable wireless channel conditions.

Abstract

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes.