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Conditional Entropy-Constrained Multi-Stage Vector Quantization for Semantic Communication
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DOI：10.1109/LWC.2025.3644153.png)
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Abstract
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This letter presents a conditional entropy-constrained multi-stage vector quantization (CEC-MSVQ) framework for semantic communication (SC). The proposed method integrates multi-stage VQ (MSVQ) for fine-grained rate control and entropy-constrained VQ (ECVQ) for improved rate–distortion efficiency. By modeling stage-wise quantization outputs as a Markovian sequence with conditional entropy modeling, CEC-MSVQ jointly trains semantic encoder–decoder networks and VQ codebooks while explicitly optimizing the rate–distortion objective. During inference, multi-rate transmission is supported by selectively activating VQ modules. Simulation results show that CEC-MSVQ achieves superior task performance over existing VQ-based SC, confirming the effectiveness of the proposed conditional entropy modeling.
Keywords:
Multi-stage vector quantization
entropy-constrained vector quantization
digital semantic communication
rate control
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