A Theoretically-Grounded Codebook for Digital Semantic Communications

2025-10-08

AI Summary

To address low quantization efficiency, poor robustness, and semantic distortion induced by channel noise in digital semantic communication, this paper proposes an information-theoretic, learnable codebook design framework. We first establish a theoretical equivalence between semantic synonym mapping and Voronoi partitioning, then formulate an end-to-end jointly optimized objective comprising a semantic-maximizing entropy-regularized quantization loss and a channel-aware semantic distortion loss. The method integrates mutual information maximization, Voronoi-based quantization modeling, and channel distortion characterization. Evaluated on image reconstruction, the proposed approach achieves a 24.1% PSNR gain and a 46.5% improvement in LPIPS perceptual similarity at 10 dB SNR, significantly mitigating semantic distortion. This work introduces a novel paradigm for efficient and reliable semantic-driven transmission.

Technology Category

• Machine Learning: Information Theory
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The use of a learnable codebook provides an efficient way for semantic communications to map vector-based high-dimensional semantic features onto discrete symbol representations required in digital communication systems. In this paper, the problem of codebook-enabled quantization mapping for digital semantic communications is studied from the perspective of information theory. Particularly, a novel theoretically-grounded codebook design is proposed for jointly optimizing quantization efficiency, transmission efficiency, and robust performance. First, a formal equivalence is established between the one-to-many synonymous mapping defined in semantic information theory and the many-to-one quantization mapping based on the codebook's Voronoi partitions. Then, the mutual information between semantic features and their quantized indices is derived in order to maximize semantic information carried by discrete indices. To realize the semantic maximum in practice, an entropy-regularized quantization loss based on empirical estimation is introduced for end-to-end codebook training. Next, the physical channel-induced semantic distortion and the optimal codebook size for semantic communications are characterized under bit-flip errors and semantic distortion. To mitigate the semantic distortion caused by physical channel noise, a novel channel-aware semantic distortion loss is proposed. Simulation results on image reconstruction tasks demonstrate the superior performance of the proposed theoretically-grounded codebook that achieves a 24.1% improvement in peak signal-to-noise ratio (PSNR) and a 46.5% improvement in learned perceptual image patch similarity (LPIPS) compared to the existing codebook designs when the signal-to-noise ratio (SNR) is 10 dB.

Problem

• Optimizing codebook design for digital semantic communication systems
• Maximizing semantic information transmission through discrete quantization mapping
• Mitigating semantic distortion caused by physical channel noise effects

Innovation

• Theoretically-grounded codebook optimizes quantization and transmission efficiency
• Mutual information maximizes semantic information in discrete indices
• Channel-aware semantic distortion loss mitigates physical noise effects