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.

翻译：空天地海一体化网络对更强健、更安全的抗干扰传输技术提出了需求。本文提出了一种用于鲁棒传输的文本语义传输框架，该框架利用先进的自然语言处理技术对句子进行建模与编码。具体而言，首先采用WordPiece算法将文本句子分割为词片，并通过基于Transformer的编码器将其嵌入为词向量以实现语义提取。编码后的数据被量化成固定长度的二进制序列进行传输，其中考虑了二进制删除信道、二进制对称信道和二进制删除信道三种传输场景。接收端利用Transformer解码器将接收到的二进制序列解码为词片，进而重构原始句子。本方法借助神经网络与注意力机制的优势，在恶劣无线环境下实现了文本数据的可靠高效传输。基于语义相似度和双语评估替补指标的仿真结果证明了所提模型在语义传输中的优越性。