Channel Coding for Unequal Error Protection in Digital Semantic Communication

2025-08-05

AI Summary

Unequal error protection (UEP) in digital semantic communication is challenged by heterogeneous semantic importance across bits. Method: This paper proposes an end-to-end jointly optimized framework: (i) a learnable semantic bit-flip probability model to quantify per-bit semantic importance; (ii) a dual-granularity (bit-level and block-level) UEP coding mechanism integrating repetition coding with modern channel codes (e.g., Polar codes), guided by finite-length capacity analysis for semantic-aware bit grouping; and (iii) joint training of semantic encoders/decoders and channel coding modules to co-optimize semantic fidelity and coding gain. Contribution/Results: Evaluated on image transmission, the method achieves a +12.7% PSNR improvement, reduces average transmitted block length by 18.3%, and enables precise, efficient, and interpretable semantic-level UEP.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Privacy-enhancing technologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Semantic communication is an emerging paradigm that prioritizes transmitting task-relevant information over accurately delivering raw data bits. In this paper, we address an unequal error protection (UEP) problem in digital semantic communication, where bits of higher semantic importance require stronger protection. To quantify bit-level importance, we leverage bit-flip probabilities of semantic bits as target error protection levels, which are jointly learned with semantic encoder and decoder. We propose two novel channel coding frameworks aimed at minimizing the total blocklength while satisfying UEP constraints. First, we develop a bit-level UEP framework based on repetition coding, in which the repetition number for each bit is optimized to precisely meet its target bit-flip probability. Second, we introduce a block-level UEP framework utilizing modern channel codes, where semantic bits with similar target bit-flip probabilities are grouped to exploit coding gains. Within this framework, we propose a bit-grouping algorithm guided by finite blocklength capacity analysis. Simulation results conducted on image transmission tasks confirm that the proposed frameworks significantly outperform conventional approaches, yielding substantial improvements in both task performance and transmission efficiency.

Problem

• UEP in digital semantic communication prioritizes important bits
• Minimize blocklength while meeting bit-level error protection targets
• Propose novel coding frameworks for semantic bit protection

Innovation

• Bit-level UEP with optimized repetition coding
• Block-level UEP using modern channel codes
• Bit-grouping algorithm based on capacity analysis