Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Packing Lemma for Polar Codes (1504.05793v3)

Published 22 Apr 2015 in cs.IT and math.IT

Abstract: A packing lemma is proved using a setting where the channel is a binary-input discrete memoryless channel $(\mathcal{X},w(y|x),\mathcal{Y})$, the code is selected at random subject to parity-check constraints, and the decoder is a joint typicality decoder. The ensemble is characterized by (i) a pair of fixed parameters $(H,q)$ where $H$ is a parity-check matrix and $q$ is a channel input distribution and (ii) a random parameter $S$ representing the desired parity values. For a code of length $n$, the constraint is sampled from $p_S(s) = \sum_{xn\in {\mathcal{X}}n} \phi(s,xn)qn(xn)$ where $\phi(s,xn)$ is the indicator function of event ${s = xn HT}$ and $qn(xn) = \prod_{i=1}nq(x_i)$. Given $S=s$, the codewords are chosen conditionally independently from $p_{Xn|S}(xn|s) \propto \phi(s,xn) qn(xn)$. It is shown that the probability of error for this ensemble decreases exponentially in $n$ provided the rate $R$ is kept bounded away from $I(X;Y)-\frac{1}{n}I(S;Yn)$ with $(X,Y)\sim q(x)w(y|x)$ and $(S,Yn)\sim p_S(s)\sum_{xn} p_{Xn|S}(xn|s) \prod_{i=1}{n} w(y_i|x_i)$. In the special case where $H$ is the parity-check matrix of a standard polar code, it is shown that the rate penalty $\frac{1}{n}I(S;Yn)$ vanishes as $n$ increases. The paper also discusses the relation between ordinary polar codes and random codes based on polar parity-check matrices.

Citations (5)

Summary

We haven't generated a summary for this paper yet.