Recursive Trellis Processing of Large Polarization Kernels

TL;DR

This paper introduces a recursive trellis-based algorithm for efficiently computing log-likelihood ratios in decoding large-kernel polar codes, enabling improved performance over traditional Arikan kernel codes at similar complexity.

Contribution

It presents a novel recursive trellis processing method that reduces complexity and enhances decoding performance for large polarization kernels.

Findings

Improved decoding performance with large kernels.

Efficient recursive trellis algorithm for LLR computation.

Comparable complexity to existing methods.

Abstract

A reduced complexity algorithm is presented for computing the log-likelihood ratios arising in the successive cancellation decoder for polar codes with large kernels of arbitrary dimension. The proposed algorithm exploits recursive trellis representation of the codes generated by submatrices of the polarization kernel, and enables codes based on large kernels to provide better performance compared to the codes based on Arikan kernel with the same decoding complexity.