Transformer-based Joint Source Channel Coding for Textual Semantic
  Communication
Shicong Liu Zhen Gao Gaojie Chen Yu Su Lu Peng
The Space-Air-Ground-Sea integrated network calls for more robust and secure
transmission techniques against jamming. In this paper, we propose a textual
semantic transmission framework for robust transmission, which utilizes the
advanced natural language processing techniques to model and encode sentences.
Specifically, the textual sentences are firstly split into tokens using
wordpiece algorithm, and are embedded to token vectors for semantic extraction
by Transformer-based encoder. The encoded data are quantized to a fixed length
binary sequence for transmission, where binary erasure, symmetric, and deletion
channels are considered for transmission. The received binary sequences are
further decoded by the transformer decoders into tokens used for sentence
reconstruction. Our proposed approach leverages the power of neural networks
and attention mechanism to provide reliable and efficient communication of
textual data in challenging wireless environments, and simulation results on
semantic similarity and bilingual evaluation understudy prove the superiority
of the proposed model in semantic transmission.