Robust Semantic Communications Against Semantic Noise
- URL: http://arxiv.org/abs/2202.03338v1
- Date: Mon, 7 Feb 2022 16:37:45 GMT
- Title: Robust Semantic Communications Against Semantic Noise
- Authors: Qiyu Hu, Guangyi Zhang, Zhijin Qin, Yunlong Cai and Guanding Yu
- Abstract summary: We first propose a framework for the robust end-to-end semantic communication systems to combat semantic noise.
We analyze the causes of semantic noise and propose a practical method to generate it.
Our proposed method significantly improves the robustness of semantic communication systems against semantic noise with significant reduction on the transmission overhead.
- Score: 34.80426719511182
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although the semantic communications have exhibited satisfactory performance
in a large number of tasks, the impact of semantic noise and the robustness of
the systems have not been well investigated. Semantic noise is a particular
kind of noise in semantic communication systems, which refers to the misleading
between the intended semantic symbols and received ones. In this paper, we
first propose a framework for the robust end-to-end semantic communication
systems to combat the semantic noise. Particularly, we analyze the causes of
semantic noise and propose a practical method to generate it. To remove the
effect of semantic noise, adversarial training is proposed to incorporate the
samples with semantic noise in the training dataset. Then, the masked
autoencoder is designed as the architecture of a robust semantic communication
system, where a portion of the input is masked. To further improve the
robustness of semantic communication systems, we design a discrete codebook
shared by the transmitter and the receiver for encoded feature representation.
Thus, the transmitter simply needs to transmit the indices of these features in
the codebook. Simulation results show that our proposed method significantly
improves the robustness of semantic communication systems against semantic
noise with significant reduction on the transmission overhead.
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