Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach

TL;DR

This paper introduces an auto-encoder based approach for designing rate-compatible linear block codes that can adapt to varying channel conditions, reducing the need for multiple models and improving performance.

Contribution

It proposes a novel AI-driven method for creating rate-compatible codes using auto-encoders, enabling efficient adaptation to different rates with a single model.

Findings

Demonstrates superior performance of the proposed codes in numerical experiments.

Shows effective optimization of coding process with multiple puncturing patterns.

Validates the adaptability of AI-based RC-LBCs across various channel conditions.

Abstract

Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments.