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IEEE Transactions on Information Theory

Separation of random number generation and resolvability

Karthik Visweswariah, Sanjeev R. Kulkarni, Sergio Verdú

Published Jan 1, 2000• 12 citations
Publisher

Abstract

We consider the problem of determining when a given source can be used to approximate the output due to any input to a given channel. We provide achievability and converse results for a general source and channel. For the special case of a full-rank discrete memoryless channel we give a stronger converse result than we can give for a general channel.

DOI
10.1109/18.868497

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We consider the problem of determining when a given source can be used to approximate the output due to any input to a given channel. We provide achievability and converse results for a general source and channel. For the special case of a full-rank discrete memoryless channel we give a stronger converse result than we can give for a general channel.
Given a channel and an input process we study the minimum randomness of those input processes whose output statistics approximate the original output statistics with arbitrary accuracy. We introduce the notion of resolvability of a channel, defined as the number of random bits required per channel use in order to generate an input that achieves arbitrarily accurate approximation of the output statistics for any given input process. We obtain a general formula for resolvability which holds regardless of the channel memory structure. We show that, for most channels, resolvability is equal to Shannon capacity. By-products of our analysis are a general formula for the minimum achievable (fixed-length) source coding rate of any finite-alphabet source, and a strong converse of the identification coding theorem, which holds for any channel that satisfies the strong converse of the channel coding theorem.
The problem of channel resolvability, where a given output probability distribution over a channel is approximated by encoding uniform random number as a channel input, is addressed. The channel resolvability has recently been generalized to the variable-length setting, where the variable-length uniform random number instead of the fixed-length one is encoded. Though the optimum resolvability rate can be reduced compared with the fixed-length resolvability, it is not yet clear how much resolvability rate can be saved even when the given source and channel are stationary and memoryless. Given a stationary memoryless source and a discrete memoryless channel, this paper establishes a single-letter formula for the variable-length resolvability under the variational distance as an approximation measure. When the channel is a full-rank discrete memoryless channel, the established formula reduces to a further simpler formula characterized by the mutual information between the source and the channel. The established formula also recovers a known formula for the variable-length source resolvability.
Necessary and sufficient conditions for approximation of a general channel by a general source are proved. For the special case in which the channel input is deterministic, which corresponds to source simulation, we prove a stronger necessary condition. As the approximation criteria, vanishing variational distance between the original and the approximated quantity is used for both of the problems. Both necessary and sufficient conditions for the two problems are based on some individual properties of the sources and the channel and are relatively easy to evaluate. In particular, unlike prior results for this problem, our results do not require solving an optimization problem to test simulatability.
Necessary and sufficient conditions for approximation of a general channel by a general source are proved. For the special case in which the channel input is deterministic, which corresponds to source simulation, we prove a stronger necessary condition. As the approximation criteria, vanishing variational distance between the original and the approximated quantity is used for both of the problems. Both necessary and sufficient conditions for the two problems are based on some individual properties of the sources and the channel and are relatively easy to evaluate. In particular, unlike prior results for this problem, our results do not require solving an optimization problem to test simulatability. The results are illustrated with several non-ergodic examples.