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PLM-Based Two-Stage Semantic Encoding for Task-Oriented Semantic Communication on the Fly
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DOI：10.1109/tccn.2026.3718954.png)
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Abstract
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Semantic communication (SemCom) moves beyond bit-level transmission by conveying semantic features, and task-oriented communication (TOC) further targets the transmission of task-relevant features to improve communication efficiency. However, existing TOC schemes still rely on high-dimensional deep learning (DL)-based semantic source encoders for each data input, leading to high encoding complexity during communication. This paper proposes a novel paradigm named task-oriented SemCom on-the-fly (TO-SemComFly), which leverages pre-trained language model (PLM) to pre-compute semantic features offline, enabling fast and low-complexity encoding on the fly. TO-SemComFly adopts a PLM-based two-stage semantic encoder, which employs a PLM-assisted coarse (PAC) semantic encoder to retrieve raw semantic features from a PLM-based cache at the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{\mathrm {st}}$ </tex-math></inline-formula> stage and a lightweight fine (LF) semantic encoder at the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{\mathrm {nd}}$ </tex-math></inline-formula> stage to form coherent and channel-robust semantic features. To achieve low complexity, the PAC semantic encoder leverages Hierarchical Navigable Small World (HNSW) algorithm for graph-based efficient search. Moreover, the LF semantic encoder is designed as a simple encoder with only two layers. Simulation results demonstrate that TO-SemComFly enhances both transmission accuracy and efficiency. Compared with existing TOC schemes, TO-SemComFly achieves at least 63.4% and 51.7% accuracy improvements at low SNR, and 11.5% and 20.7% efficiency improvements in AWGN and Rayleigh fading channels, respectively.
Keywords:
Task-oriented semantic communication
pre-trained language model
two-stage encoding
hierarchical navigable small world
transmission accuracy and efficiency
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1.5K
Citations:
5.5K
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