Deep Learning-Enabled Semantic Communication Systems with Task-Unaware
Transmitter and Dynamic Data
- URL: http://arxiv.org/abs/2205.00271v1
- Date: Sat, 30 Apr 2022 13:45:50 GMT
- Title: Deep Learning-Enabled Semantic Communication Systems with Task-Unaware
Transmitter and Dynamic Data
- Authors: Hongwei Zhang, Shuo Shao, Meixia Tao, Xiaoyan Bi, and Khaled B.
Letaief
- Abstract summary: This paper proposes a new neural network-based semantic communication system for image transmission.
The proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.
- Score: 43.308832291174106
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Existing deep learning-enabled semantic communication systems often rely on
shared background knowledge between the transmitter and receiver that includes
empirical data and their associated semantic information. In practice, the
semantic information is defined by the pragmatic task of the receiver and
cannot be known to the transmitter. The actual observable data at the
transmitter can also have non-identical distribution with the empirical data in
the shared background knowledge library. To address these practical issues,
this paper proposes a new neural network-based semantic communication system
for image transmission, where the task is unaware at the transmitter and the
data environment is dynamic. The system consists of two main parts, namely the
semantic extraction (SE) network and the data adaptation (DA) network. The SE
network learns how to extract the semantic information using a receiver-leading
training process. By using domain adaptation technique from transfer learning,
the DA network learns how to convert the data observed into a similar form of
the empirical data that the SE network can process without re-training.
Numerical experiments show that the proposed method can be adaptive to
observable datasets while keeping high performance in terms of both data
recovery and task execution. The codes are available on
https://github.com/SJTU-mxtao/Semantic-Communication-Systems.
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