A Robust Semantic Text Communication System
Xiang Peng, Zhijin Qin, Xiaoming Tao, Jianhua Lü, Lajos Hanzo
Abstract
Semantic communication is increasingly viewed as a promising solution to improve the transmission efficiency. However, semantic communications are susceptible not only to physical channel impairments, but also to semantic impairments, which degrade semantic understanding at the receiver and disrupt the associated downstream tasks. Hence, we focus our attention on the robustness of semantic communications against semantic impairments. Specifically, we first categorize textual semantic impairments into three categories based on their sources. Then, we propose a robust deep learning enabled semantic communication system (R-DeepSC) by introducing a semantic corrector for robust semantic encoding so as to facilitate semantic transmission. Moreover, we develop a non-autoregressive version of R-DeepSC, namely NA-RDeepSC, which offers improved inference speed by relying on a non-autoregressive architecture and an adaptive generator embedded into the semantic decoder. NA-RDeepSC performs semantic decoding in parallel, hence reducing the decoding complexity from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1) with a comparable performance to that of R-DeepSC. Our experimental results demonstrate the superior robustness of the proposed R-DeepSC and NA-RDeepSC architectures in eliminating semantic impairments, hence highlighting the significance of this work in advancing the development of robust semantic communications.