English

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

Information Theory 2017-11-06 v2 math.IT

Abstract

For any prime power qq, Mori and Tanaka introduced a family of qq-ary polar codes based on qq~by~qq Reed-Solomon polarization kernels. For transmission over a qq-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 qq-ary erasure channel with erasure probability ϵ(0,1)\epsilon\in(0,1). Our primary result is that, for any γ>0\gamma>0 and δ>0\delta>0, there is a q0q_{0} such that, for all qq0q\geq q_{0}, the fraction of effective channels with erasure rate at most NγN^{-\gamma} is at least 1ϵO(N1/2+δ)1-\epsilon-O(N^{-1/2+\delta}), where N=qnN=q^{n} is the blocklength. Since this fraction cannot be larger than 1ϵO(N1/2)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 qq-ary polar codes with mm by mm 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 mm by mm 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 qq as mm 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~qq.

Keywords

Cite

@article{arxiv.1605.01997,
  title  = {Near-Optimal Finite-Length Scaling for Polar Codes over Large Alphabets},
  author = {Henry D. Pfister and Rüdiger Urbanke},
  journal= {arXiv preprint arXiv:1605.01997},
  year   = {2017}
}

Comments

Extended version of a paper presented ISIT 2016. Submitted to IEEE Transactions on Information Theory, Oct. 2017