Short Wins Long: Short Codes with Language Model Semantic Correction Outperform Long Codes

TL;DR

This paper introduces a semantic-enhanced decoding scheme using short codes and a BART-based error correction model, outperforming traditional long code methods in wireless communication of natural language.

Contribution

The paper proposes a novel combination of short block codes with a semantic error correction model for improved wireless natural language transmission.

Findings

Significant reduction in block error rate compared to long LDPC codes

Enhanced semantic accuracy in reconstructed messages

Reduced decoding latency with short codes

Abstract

This paper presents a novel semantic-enhanced decoding scheme for transmitting natural language sentences with multiple short block codes over noisy wireless channels. After ASCII source coding, the natural language sentence message is divided into segments, where each is encoded with short block channel codes independently before transmission. At the receiver, each short block of codewords is decoded in parallel, followed by a semantic error correction (SEC) model to reconstruct corrupted segments semantically. We design and train the SEC model based on Bidirectional and Auto-Regressive Transformers (BART). Simulations demonstrate that the proposed scheme can significantly outperform encoding the sentence with one conventional long LDPC code, in terms of block error rate (BLER), semantic metrics, and decoding latency. Finally, we proposed a semantic hybrid automatic repeat request (HARQ) scheme to further enhance the error performance, which selectively requests retransmission depends on semantic uncertainty.