# A Regression Approach to Certain Information Transmission Problems

arXiv: https://arxiv.org/abs/1906.03777 · HTML: https://mlsanity.com/paper/1906.03777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [An Information-Theoretic Framework for Receiver Quantization in Communication](https://mlsanity.com/paper/2505.12258/) (2025)

## Abstract (arXiv)

A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information. When the encoder and the decoder know the statistical channel model, it is found that the optimal channel output processing function is the conditional expectation operator, thus hinting a potential role of regression, a classical topic in machine learning, for this model. Without utilizing the statistical channel model, a problem formulation inspired by machine learning principles is established, with suitable performance metrics introduced. A data-driven inference algorithm is proposed to solve the problem, and the effectiveness of the algorithm is validated via numerical experiments. Extensions to more general information transmission models are also discussed.