# Channel Estimation by Infinite Width Convolutional Networks

arXiv: https://arxiv.org/abs/2504.08660 · HTML: https://mlsanity.com/paper/2504.08660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent](https://mlsanity.com/paper/1902.06720/) (2019)
- [On Exact Computation with an Infinitely Wide Neural Net](https://mlsanity.com/paper/1904.11955/) (2019)
- [Gaussian Process Behaviour in Wide Deep Neural Networks](https://mlsanity.com/paper/1804.11271/) (2018)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Uniform convergence may be unable to explain generalization in deep learning](https://mlsanity.com/paper/1902.04742/) (2019)
- [Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes](https://mlsanity.com/paper/1810.05148/) (2018)
- [Neural Tangents: Fast and Easy Infinite Neural Networks in Python](https://mlsanity.com/paper/1912.02803/) (2019)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks](https://mlsanity.com/paper/2108.00131/) (2021)
- [Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN](https://mlsanity.com/paper/2006.11435/) (2020)
- [Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2104.09023/) (2021)
- [CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications](https://mlsanity.com/paper/2104.08813/) (2021)
- [GLIP: Electromagnetic Field Exposure Map Completion by Deep Generative Networks](https://mlsanity.com/paper/2405.03384/) (2024)
- [An Efficient Machine Learning-based Channel Prediction Technique for OFDM Sub-Bands](https://mlsanity.com/paper/2305.19696/) (2023)

## Abstract (arXiv)

In wireless communications, estimation of channels in OFDM systems spans frequency and time, which relies on sparse collections of pilot data, posing an ill-posed inverse problem. Moreover, deep learning estimators require large amounts of training data, computational resources, and true channels to produce accurate channel estimates, which are not realistic. To address this, a convolutional neural tangent kernel (CNTK) is derived from an infinitely wide convolutional network whose training dynamics can be expressed by a closed-form equation. This CNTK is used to impute the target matrix and estimate the missing channel response using only the known values available at pilot locations. This is a promising solution for channel estimation that does not require a large training set. Numerical results on realistic channel datasets demonstrate that our strategy accurately estimates the channels without a large dataset and significantly outperforms deep learning methods in terms of speed, accuracy, and computational resources.