High Signal-to-Noise Ratio Asymptotics of Entropy-Constrained Gaussian Channel Capacity
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.
Fields of Study & Subjects
Error Correcting Code Techniques
Advanced Wireless Communication Techniques
Advanced Data Compression Techniques
Publication details
- Journal / source
- IEEE International Symposium on Information Theory (ISIT)
- Pages
- 1–6
- Publication date
- 1 June 2026