A Training-Based Mutual Information Lower Bound for Large-Scale Systems
Information Theory
2021-08-21 v1 math.IT
Abstract
We provide a mutual information lower bound that can be used to analyze the effect of training in models with unknown parameters. For large-scale systems, we show that this bound can be calculated using the difference between two derivatives of a conditional entropy function. The bound does not require explicit estimation of the unknown parameters. We provide a step-by-step process for computing the bound, and provide an example application. A comparison with known classical mutual information bounds is provided.
Cite
@article{arxiv.2108.00034,
title = {A Training-Based Mutual Information Lower Bound for Large-Scale Systems},
author = {Xiangbo Meng and Kang Gao and Bertrand M. Hochwald},
journal= {arXiv preprint arXiv:2108.00034},
year = {2021}
}
Comments
This work has been submitted to the IEEE for possible publication. arXiv admin note: substantial text overlap with arXiv:2012.00970