Channel Estimation by Infinite Width Convolutional Networks
This addresses the challenge of channel estimation for wireless communications, offering a more efficient solution by reducing data and computational requirements, though it is incremental as it builds on existing kernel methods.
The paper tackles the problem of channel estimation in OFDM wireless systems, which is ill-posed due to sparse pilot data, by using a convolutional neural tangent kernel (CNTK) derived from an infinitely wide convolutional network, resulting in accurate channel estimates without large datasets and outperforming deep learning methods in speed, accuracy, and computational resources.
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.