Semantic Error Correction and Decoding for Short Block Codes
Jiafu Hao, Chentao Yue, Wanchun Liu, Branka Vucetic, Yonghui Li

TL;DR
This paper introduces a semantic-enhanced receiver framework for transmitting natural language over noisy channels, utilizing short block codes, semantic error correction, list decoding, and confidence-guided retransmission to improve fidelity and reduce latency.
Contribution
It proposes a novel semantic-aware transmission scheme with SEC, SLD, and SHARQ modules trained using transformer models, outperforming conventional coding methods.
Findings
SEC yields 0.4 dB BLER gain over plain short codes.
SLD extends gain to 0.8 dB and improves semantic fidelity.
SHARQ adds 1.5 dB gain over traditional HARQ.
Abstract
This paper presents a semantic-enhanced receiver framework for transmitting natural language sentences over noisy wireless channels using multiple short block codes. After ASCII encoding, the sentence is divided into segments, each independently encoded with a short block code and transmitted over an AWGN channel. At the receiver, segments are decoded in parallel, followed by a semantic error correction (SEC) model, which reconstructs corrupted segments using language model context. We further propose the semantic list decoding (SLD), which generates multiple candidate reconstructions and selects the best one via weighted Hamming distance, and a semantic confidence-guided HARQ (SHARQ) mechanism that replaces CRC-based error detection with a confidence score, enabling selective segment retransmission without CRC overhead. All modules are designed and trained using bidirectional and…
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