Robust Semantic Communications with Masked VQ-VAE Enabled Codebook

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus c...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 22; no. 12; p. 1
Main Authors: Hu, Qiyu, Zhang, Guangyi, Qin, Zhijin, Cai, Yunlong, Yu, Guanding, Li, Geoffrey Ye
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adversarial training
Communication systems
Communications systems
feature importance module (FIM)
masked vector quantized-variational autoencoder (VQ-VAE)
Receivers
robust semantic communications
Robustness
semantic noise
Semantics
Symbols
Task analysis
Training
Transmitters
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3265201