Language Model-Aided Text Semantic Communications for Digital-Twin Interaction

Reliable semantic interaction between physical entities and their virtual counterparts is fundamental to digital-twin operation. In challenging wireless environments, however, channel noise and fading corrupt continuous semantic representations, causing semantic drift, token substitutions, repetitive generation, and premature termination at the receiver. This article proposes LM-DeepSC, a language model-aided text semantic communication framework for digital twins. The framework combines end-to-end joint source–channel semantic transmission with a trainable continuous semantic feature adapter at the receiver. The adapter projects channel-corrupted features produced by the semantic decoder into continuous conditioning representations that a frozen pretrained language model can directly exploit. Consequently, the receiver jointly exploits residual communication evidence, contextual dependencies, and pretrained linguistic knowledge to reconstruct the source text without first committing to an error-prone intermediate token sequence. Experiments over additive white Gaussian noise and Rayleigh fading channels show that LM-DeepSC consistently outperforms DeepSC in multi-order BLEU scores and sentence similarity under the same transmitted channel-symbol budget. Evaluations over ten independent AWGN channel realizations further demonstrate a relative reduction of 46.3–71.0% in token substitution rate. On MASSIVE control instructions unseen during communication-model training, LM-DeepSC improves downstream intent-classification accuracy by 6.3–15.5 percentage points over DeepSC. These results demonstrate the effectiveness of the proposed LM-assisted receiver for robust text interaction under noisy wireless channels, with potential application to digital-twin systems.