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arXiv · 2604.21484

HyperCEUNet: Parameter-Aware Hypernetwork-Driven UNet for Channel Estimation

Ke Ma·Feng Wang·Lihui Lei·Shu Tan
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Abstract

Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-squares estimates obtained from demodulation reference signals, which fail to explicitly exploit channel time-frequency correlation parameters. Inspired by the independent channel parameter estimation enabled by semi-static reference signals in modern wireless systems, this letter presents a parameter-aware deep learning-based channel estimation framework termed HyperCEUNet. Specifically, the proposed hypernetwork generates an adaptive front-end convolutional layer based on estimated channel parameters, serving as a pre-filtering stage before the UNet-based estimator. In addition, the Wiener-filtered channel estimates are adopted to provide a correlation-aware initialization for data resources. Simulation results demonstrate that our proposed HyperCEUNet effectively improves channel estimation accuracy compared with its conventional counterparts.

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Ke Ma, Feng Wang, Lihui Lei, Shu Tan. 2026-04-23. HyperCEUNet: Parameter-Aware Hypernetwork-Driven UNet for Channel Estimation. https://arxiv.org/abs/2604.21484

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