Semantic decoding improves polar codes for language data
Semantic SCL Decoding for Polar Codes
Information Theory
Summary
Communicating data accurately over noisy channels is important, especially for transmitting language text. The authors designed a new way to decode polar codes—a type of error-correcting code—by using a language model that predicts likely next bytes based on the text's meaning. This approach helps correct errors better by considering both the communication channel and the structure of language. Their method showed much lower error rates and faster decoding times compared to traditional techniques in computer simulations.
What this means in practice
- •For telecommunications engineers: Enhance reliability of polar-coded transmissions carrying natural-language text by integrating language-aware decoding to reduce errors and decoding time.
- •For software developers of communication systems: Implement faster and more accurate polar code decoders using language model guidance selectively on uncertain bytes to trade-off latency and performance.
Authors
Andy Huang, Chentao Yue, Branka Vucetic, Yonghui Li
Abstract
Semantic error control coding (SECC) has emerged as a promising paradigm for improving communication reliability by exploiting the inherent structure and semantic dependencies of transmitted information. This paper proposes a semantic successive cancellation list (SCL) decoder for polar codes carrying natural-language text. A fine-tuned byte-level language model provides next-byte priors, which are converted into conditional bit costs and incorporated directly into the SCL path metric before each pruning step. This allows the decoder to jointly exploit channel reliability and source structure while retaining the conventional polar coding framework. The language model can also be invoked only for bytes whose channel decisions are unreliable, trading decoding performance for latency. Simulations for length-512 polar codes show substantial block error rate (BLER) improvements over conventional CRC-aided SCL (CA-SCL) decoding. At rate R=0.75 and list size L=16, the proposed decoder achieves more than 1 dB of gain in BLER compared to CA-SCL with the same L. Moreover, with L=8, it outperforms conventional CA-SCL with L=256 in terms of BLER, while requiring about one quarter of the decoding time of the latter in the evaluated software implementation.