🤖 AI Summary
Channel estimation in OFDM systems via sparse time–frequency pilot sampling is severely ill-posed due to pilot sparsity and rapid channel time-variations; existing deep learning approaches rely heavily on large-scale labeled datasets, accurate channel priors, and high computational resources. Method: We propose a novel kernel-based method that requires neither labeled data nor channel prior knowledge. Specifically, we introduce the Convolutional Neural Tangent Kernel (CNTK) for channel matrix interpolation, leveraging infinite-width CNN theory to reformulate training dynamics as a closed-form kernel ridge regression solution. Contribution/Results: Evaluated on real-world channel datasets, our method achieves high-accuracy channel estimation while accelerating inference by over 10× and reducing GPU memory consumption by 90%, significantly outperforming state-of-the-art deep learning baselines.
📝 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.