2010IEEE Transactions on Information TheoryRequires access

Channel Coding Rate in the Finite Blocklength Regime

Yury Polyanskiy, H. Vincent Poor, Sergio Verdú

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Abstract

This paper investigates the maximal channel coding rate achievable at a given blocklength and error probability. For general classes of channels new achievability and converse bounds are given, which are tighter than existing bounds for wide ranges of parameters of interest, and lead to tight approximations of the maximal achievable rate for blocklengthsnas short as 100. It is also shown analytically that the maximal rate achievable with error probability¿isclosely approximated by C - ¿(V/n) Q-1(¿) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion , and Q is the complementary Gaussian cumulative distribution function.

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What this paper is about

This paper investigates the maximal channel coding rate achievable at a given blocklength and error probability. For general classes of channels new achievability and converse bounds are given, which are tighter than existing bounds for wide ranges of parameters of interest, and lead to tight approximations of the maximal achievable rate for blocklengthsnas short as 100. It is also shown analytically that the maximal rate achievable with error probability¿isclosely approximated by C - ¿(V/n) Q-1(¿) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion , and Q is the complementary Gaussian cumulative distribution function.

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Available abstract

This paper investigates the maximal channel coding rate achievable at a given blocklength and error probability. For general classes of channels new achievability and converse bounds are given, which are tighter than existing bounds for wide ranges of parameters of interest, and lead to tight approximations of the maximal achievable rate for blocklengthsnas short as 100. It is also shown analytically that the maximal rate achievable with error probability¿isclosely approximated by C - ¿(V/n) Q-1(¿) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion , and Q is the complementary Gaussian cumulative distribution function.

Key concepts: Converse, Channel (broadcasting), Coding (social sciences), Probability of error, Gaussian, Discrete mathematics, Algorithm, Mathematics

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