# Theoretical Analysis of Deep Neural Networks in Physical Layer Communication

arXiv: https://arxiv.org/abs/2202.09954 · HTML: https://mlsanity.com/paper/2202.09954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Gradient Descent Provably Optimizes Over-parameterized Neural Networks](https://mlsanity.com/paper/1810.02054/) (2018)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://mlsanity.com/paper/1811.03804/) (2018)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Distributed Variational Representation Learning](https://mlsanity.com/paper/1807.04193/) (2018)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018)
- [Understanding Autoencoders with Information Theoretic Concepts](https://mlsanity.com/paper/1804.00057/) (2018)
- [On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views](https://mlsanity.com/paper/2002.00008/) (2020)
- [Physical Layer Communications System Design Over-the-Air Using Adversarial Networks](https://mlsanity.com/paper/1803.03145/) (2018)
- [Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs](https://mlsanity.com/paper/2008.03757/) (2020)
- [One-Bit OFDM Receivers via Deep Learning](https://mlsanity.com/paper/1811.00971/) (2018)
- [Performance Analysis on Machine Learning-Based Channel Estimation](https://mlsanity.com/paper/1911.03886/) (2019)
- [Opening the Black Box of Deep Neural Networks in Physical Layer Communication](https://mlsanity.com/paper/2106.01124/) (2021)
- [A Low Complexity Learning-based Channel Estimation for OFDM Systems with Online Training](https://mlsanity.com/paper/2107.06712/) (2021)
- [Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach](https://mlsanity.com/paper/2007.09248/) (2020)

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

- [Detecting Malicious Pilot Contamination in Multiuser Massive MIMO Using Decision Trees](https://mlsanity.com/paper/2510.03831/) (2025)

## Abstract (arXiv)

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the "black box" of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.