Near-Optimal Finite-Length Scaling for Polar Codes over Large Alphabets

TL;DR

This paper analyzes the finite-length scaling of q-ary polar codes over erasure channels, demonstrating near-optimal performance as alphabet size increases, extending previous binary code results.

Contribution

It extends finite-length scaling analysis to q-ary polar codes with large alphabets, providing near-optimal bounds and conjectures for fixed alphabet sizes.

Findings

Fraction of effective channels with low erasure rate approaches 1 as blocklength increases.

Establishes near-optimal finite-length scaling for large q-ary polar codes.

Provides mathematical conjectures for fixed alphabet size scaling.

Abstract

For any prime power $q$, Mori and Tanaka introduced a family of $q$-ary polar codes based on $q$~by~$q$ Reed-Solomon polarization kernels. For transmission over a $q$-ary erasure channel, they also derived a closed-form recursion for the erasure probability of each effective channel. In this paper, we use that expression to analyze the finite-length scaling of these codes on the $q$-ary erasure channel with erasure probability $\epsilon\in(0,1)$. Our primary result is that, for any $\gamma>0$ and $\delta>0$, there is a $q_{0}$ such that, for all $q\geq q_{0}$, the fraction of effective channels with erasure rate at most $N^{-\gamma}$ is at least $1-\epsilon-O(N^{-1/2+\delta})$, where $N=q^{n}$ is the blocklength. Since this fraction cannot be larger than $1-\epsilon-O(N^{-1/2})$, this establishes near-optimal finite-length scaling for this family of codes. Our approach can be seen as an extension of a similar analysis for binary polar codes by Hassani, Alishahi, and Urbanke. A similar analysis is also considered for $q$-ary polar codes with $m$ by $m$ polarizing matrices. This separates the effect of the alphabet size from the effect of the matrix size. If the polarizing matrix at each stage is drawn independently and uniformly from the set of invertible $m$ by $m$ matrices, then the linear operator associated with the Lyapunov function analysis can be written in closed form. To prove near-optimal scaling for polar codes with fixed $q$ as $m$ increases, however, two technical obstacles remain. Thus, we conclude by stating two concrete mathematical conjectures that, if proven, would imply near-optimal scaling for fixed~$q$.