Gaussian Channel Simulation with Rotated Dithered Quantization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916328769585152 |
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| author | Kobus, Szymon Theis, Lucas Gündüz, Deniz |
| author_facet | Kobus, Szymon Theis, Lucas Gündüz, Deniz |
| contents | Channel simulation involves generating a sample $Y$ from the conditional distribution $P_{Y|X}$, where $X$ is a remote realization sampled from $P_X$. This paper introduces a novel approach to approximate Gaussian channel simulation using dithered quantization. Our method concurrently simulates $n$ channels, reducing the upper bound on the excess information by half compared to one-dimensional methods. When used with higher-dimensional lattices, our approach achieves up to six times reduction on the upper bound. Furthermore, we demonstrate that the KL divergence between the distributions of the simulated and Gaussian channels decreases with the number of dimensions at a rate of $O(n^{-1})$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12970 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gaussian Channel Simulation with Rotated Dithered Quantization Kobus, Szymon Theis, Lucas Gündüz, Deniz Information Theory Channel simulation involves generating a sample $Y$ from the conditional distribution $P_{Y|X}$, where $X$ is a remote realization sampled from $P_X$. This paper introduces a novel approach to approximate Gaussian channel simulation using dithered quantization. Our method concurrently simulates $n$ channels, reducing the upper bound on the excess information by half compared to one-dimensional methods. When used with higher-dimensional lattices, our approach achieves up to six times reduction on the upper bound. Furthermore, we demonstrate that the KL divergence between the distributions of the simulated and Gaussian channels decreases with the number of dimensions at a rate of $O(n^{-1})$. |
| title | Gaussian Channel Simulation with Rotated Dithered Quantization |
| topic | Information Theory |
| url | https://arxiv.org/abs/2407.12970 |