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Two-Way Semantic Communications Without Feedback
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AI
DOI：10.1109/TVT.2024.3352666.png)
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Abstract
En 中文
Semantic communications can significantly improve transmission efficiency, especially in the low signal-to-noise (SNR) regime. However, two-way semantic communications still remain an unexplored topic, which is a critical issue in many machine communication scenarios. Simply extending existing semantic communication systems to the two-way situation requires bidirectional information feedback during the training process, resulting in significant communication overhead. To fill this gap, we investigate a two-way semantic communication (TW-SC) system, where the information feedback can be omitted by exploiting the weight reciprocity in the transceiver. Particularly, the channel simulator and semantic transceiver are implemented on both TW-SC nodes and the channel distribution is modeled by a conditional generative adversarial network. Simulation results demonstrate that the proposed TW-SC system performs closing to the state-of-the-art one-way semantic communication systems but requiring no feedback between the transceiver in training process.
Keywords:
Semantics
Training
Transmitters
Receivers
Wireless communication
Generators
Artificial neural networks
Semantic communications
deep learning
joint source-channel coding
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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