Joint Source-Channel-Check Coding with HARQ for Reliable Semantic Communications

2026-03-24

AI Summary

This work addresses the limitations of existing semantic communication systems, which rely on separate parity-check codes to trigger retransmissions—resulting in high overhead and vulnerability to errors in semantic quality estimation. To overcome these issues, the authors propose the S3CHARQ framework, which uniquely integrates parity-check codes into joint source-channel coding (JS3C), enabling simultaneous semantic fidelity verification and reconstruction enhancement. Furthermore, they introduce a reinforcement learning–based adaptive retransmission decision mechanism that optimizes retransmission policies at the sample level. Compared to conventional HARQ-based semantic communication systems, the proposed approach improves the 97th-percentile PSNR by 2.36 dB and reduces outage probability by 37.45%, thereby transcending the traditional role of parity-check codes as mere retransmission triggers and significantly enhancing both reliability and efficiency.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Semantic communication has emerged as a promising paradigm for improving transmission efficiency and task-level reliability, yet most existing reliability-enhancement approaches rely on retransmission strategies driven by semantic fidelity checking that require additional check codewords solely for retransmission triggering, thereby incurring substantial communication overhead. In this paper, we propose S3CHARQ, a Joint Source-Channel-Check Coding framework with hybrid automatic repeat request that fundamentally rethinks the role of check codewords in semantic communications. By integrating the check codeword into the JSCC process, S3CHARQ enables JS3C, allowing the check codeword to simultaneously support semantic fidelity verification and reconstruction enhancement. At the transmitter, a semantic fidelity-aware check encoder embeds auxiliary reconstruction information into the check codeword. At the receiver, the JSCC and check codewords are jointly decoded by a JS3C decoder, while the check codeword is additionally exploited for perceptual quality estimation. Moreover, because retransmission decisions are necessarily based on imperfect semantic quality estimation in the absence of ground-truth reconstruction, estimation errors are unavoidable and fundamentally limit the effectiveness of rule-based decision schemes. To overcome this limitation, we develop a reinforcement learning-based retransmission decision module that enables adaptive, sample-level retransmission decisions, effectively balancing recovery and refinement information under dynamic channel conditions. Experimental results demonstrate that compared with existing HARQ-based semantic communication systems, the proposed S3CHARQ framework achieves a 2.36 dB improvement in the 97th percentile PSNR, as well as a 37.45% reduction in outage probability.

Problem

• Semantic Communication
• Reliability
• HARQ
• Check Codeword
• Communication Overhead

Innovation

• Joint Source-Channel-Check Coding
• Semantic Communication
• HARQ
• Reinforcement Learning-based Retransmission
• Perceptual Quality Estimation