用无限宽卷积网络实现无需大量训练数据的精准信道估计
Channel Estimation by Infinite Width Convolutional Networks
- 基于无限宽卷积网络推导出可解析求解的神经正切核
- 仅用导频数据即可高精度还原缺失信道响应，误差显著低于深度学习方法
- 适合资源受限场景，特别适用于真实无线信道数据的快速估计
在无线通信中，正交频分复用（OFDM）系统的信道估计涉及频率与时间维度的联合建模，依赖稀疏导频数据，构成病态逆问题。传统深度学习方法需大量训练数据、计算资源及真实信道信息，不具实用性。为此，本文从无限宽卷积网络推导出卷积神经正切核（CNTK），其训练动态可用闭式方程描述。利用该核对目标矩阵进行插值，仅基于导频位置已知值即可估计缺失的信道响应。在真实信道数据集上的数值实验表明，该方法无需大规模数据集即可实现高精度信道估计，在速度、准确率和计算开销上均显著优于深度学习方法。
原文摘要 · Abstract (English)
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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