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

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 de...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 23; no. 8; pp. 8388 - 8404
Main Authors: Lyu, Zhonghao, Zhu, Guangxu, Xu, Jie, Ai, Bo, Cui, Shuguang
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial intelligence
Channel coding
Classification
Clustering
Data mining
Data recovery
deep joint source and channel coding (JSCC)
Deep learning
Edge artificial intelligence (AI)
Error reduction
Feature extraction
Image coding
Image quality
Labels
Logic gates
Machine learning
Maximization
Optimization
semantic communications
Semantics
Task analysis
task-oriented communications
ISSN:1536-1276, 1558-2248
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Summary: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. It is shown that our proposed designs simultaneously provide efficient multi-task services, and the proposed gated deep JSCC framework efficiently reduces the communication overhead with only marginal performance loss. It is also shown that performing the classification task on the feature space via coding rate reduction maximization is able to better defend the label corruption than the traditional label-fitting methods.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3349330