.png){kind=icon}
Return
AI-Driven Rate-Adaptive LDPC Coding and Decoding
.png){kind=icon}
PRE
AI
DOI：10.1109/mwc.2026.3723359.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Artificial intelligence (AI)–driven channel coding has emerged as a promising solution for enhancing error correction performance and reducing decoding complexity, particularly in the context of Low-Density Parity-Check (LDPC) codes. Unlike traditional coding-theory-based encoding and decoding methods, AI-driven approaches optimise codeword design in a data-driven manner under conditions where classical techniques have limitations, e.g., LDPC codes with limited block lengths and finite decoding complexity. AI-designed LDPC codewords and AI-based decoding algorithms are usually optimised for a given block length or code rate, and cannot be directly applied to different block lengths or rates. To this end, this article presents an AI-driven rate-adaptive LDPC coding framework that incorporates a parameter-sharing mechanism within the encoder and decoder, trained via multi-task learning to support multiple code rates. Preliminary evaluations in AWGN channels validate the feasibility of such a design and demonstrate its promising error-correction potential and adaptability. In this article, we present key design principles, emphasise their advantages, and discuss the future research challenges that remain.
Keywords:
Artificial intelligence
low-density parity-check code
rate-compatible
belief propagation
Journal
IF:
11.5
Papers:
2.8K
Citations:
1.3W
Organization
Cited Papers
No cited papers available