Channel Estimation by Infinite Width Convolutional Networks

• URL: http://arxiv.org/abs/2504.08660v1
• Date: Fri, 11 Apr 2025 16:01:17 GMT
• Title: Channel Estimation by Infinite Width Convolutional Networks
• Authors: Mohammed Mallik, Guillaume Villemaud,
• Abstract summary: In wireless communications, estimation of channels in OFDM systems spans frequency and time.<n>Deep learning estimators require large amounts of training data, computational resources, and true channels to produce accurate channel estimates.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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