Achieving Robust Channel Estimation Neural Networks by Designed Training Data

Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the channel state information. Current channel estimation neural networks are frequently tested by training and testing on one example channel or similar channels. However, data-driven methods often degrade on new data which they are not trained on, because they cannot extrapolate their training knowledge. This motivates us to investigate how to achieve neural network solutions that perform robustly over a wide range of realistic channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on a wide range of unseen channels. Therefore, these neural network implementations require no prior information of channels or parameters update for real-world deployment. Based on the proposed design criteria, we further propose a benchmark training dataset design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to demonstrate the generalization achieved. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads. 

信道估计在认知通信（Cognitive Communications）中至关重要，其通过提供精准的信道状态信息（Channel State Information, CSI），支撑智能频谱感知与自适应传输任务。当前的信道估计神经网络（Neural Network）通常仅在单类典型信道或相似信道集合上完成训练与测试。然而，数据驱动方法往往在未参与训练的新数据上出现性能退化，这是因为它们无法外推训练阶段习得的知识。这一痛点促使我们探究如何在设计阶段无需任何实际信道信息的前提下，获得可在大量真实信道场景下稳健运行的神经网络解决方案。本文提出了面向神经网络的合成训练数据集（Synthetic Training Dataset）设计准则，可确保训练后的神经网络在大量未见过的信道场景下达到指定的均方误差（Mean Squared Error, MSE）性能指标。因此，该类神经网络在实际部署时无需预先知晓信道信息，也无需进行参数更新。基于所提出的设计准则，本文进一步提出了基准训练数据集设计方案，可确保神经网络在不同信道特性下实现智能运行。为验证方法的普适性，本文采用不同复杂度层级的神经网络来展示所实现的泛化性能。仿真结果表明，所提出的神经网络可在固定信道特性与时变时延扩展的无线信道场景下均实现稳健的泛化性能。