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中至关重要，因其能通过提供关于信道状态信息的准确信息，实现智能频谱感知和自适应传输。目前，信道估计神经网络往往通过在一个示例信道或相似信道上进行训练和测试来进行验证。然而，由于数据驱动方法无法外推其训练知识，它们在未经训练的新数据上往往性能下降。这促使我们探究如何实现能够在广泛真实信道上稳健运行的神经网络解决方案，而无需在设计时知晓任何信道信息。在本文中，我们提出了生成合成训练数据集的设计标准，以确保经过训练的神经网络在广泛未见的信道上达到一定的均方误差（MSE）。因此，这些神经网络实现无需对信道或参数更新进行任何先验信息，即可适用于现实世界的部署。基于提出的设计标准，我们进一步提出了一个基准训练数据集的设计方案，以确保不同信道配置文件下的智能操作。为了展示其通用适用性，我们使用不同复杂程度的神经网络来展示所达到的泛化能力。从仿真结果来看，神经网络能够对具有固定信道配置和可变延迟扩展的无线信道实现稳健的泛化。