Publication

High Signal-to-Noise Ratio Asymptotics of Entropy-Constrained Gaussian Channel Capacity

2026

Abstract

We study the input-entropy constrained Gaussian channel capacity problem in the asymptotic high signal-to-noise ratio (SNR) regime. We show that the capacity-achieving distribution as SNR goes to infinity is given by a discrete Gaussian distribution supported on a scaled integer lattice. Further, we show that the gap between the input entropy and the capacity decreases to zero exponentially in SNR, and characterize this exponent.

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2026

Abstract

Official source

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High Signal-to-Noise Ratio Asymptotics of Entropy-Constrained Gaussian Channel Capacity

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