ITAIJan 30, 2024

Nested Construction of Polar Codes via Transformers

arXiv:2401.17188v113 citationsh-index: 7ISIT
Originality Incremental advance
AI Analysis

This work addresses polar code construction for decoding algorithms beyond successive cancellation, which is important for communication systems, but appears incremental as it applies transformers to an understudied aspect of a known problem.

The paper tackles the problem of constructing polar codes for various channel conditions by proposing a sequence modeling framework using transformers, which outperforms existing 5G-NR sequence and Density Evolution methods in simulations for AWGN and Rayleigh fading channels.

Tailoring polar code construction for decoding algorithms beyond successive cancellation has remained a topic of significant interest in the field. However, despite the inherent nested structure of polar codes, the use of sequence models in polar code construction is understudied. In this work, we propose using a sequence modeling framework to iteratively construct a polar code for any given length and rate under various channel conditions. Simulations show that polar codes designed via sequential modeling using transformers outperform both 5G-NR sequence and Density Evolution based approaches for both AWGN and Rayleigh fading channels.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes