Dataset for the manuscript of Analysis on constructing the training data to train neural networks for channel estimation

Current neural network solutions for channel estimation are frequently tested by training and testing on one example channel or similar channels. However, data-driven algorithms often degrade significantly on other channels which they are not trained on, because they cannot extrapolate their training knowledge. Online training can fine-tune the offline-trained neural networks to compensate for this degradation, but its feasibility is challenged by the tremendous computational resources required. To solve this degradation, we propose design criteria to generate training datasets which will ensure neural networks robustly generalize to different channels. This design criteria also constrains the channels that the trained neural networks can generalize to. Based on the proposed criteria, we further propose a benchmark design to provide maximum generalization. In this way, the offline-trained neural networks still predict the complete channel matrix to achieve both the frequency and time interpolation, even on these channels which they are not trained on. To show the general applicability, neural networks with different levels of complexity are employed to demonstrate the generalization achieved. From the simulations, the trained neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads. We also investigate the noise effect of the training dataset and the system scalability. This paper indicates one step towards artificial general intelligence (AGI) from new perspectives.

当前针对信道估计的神经网络解决方案，往往通过在一例信道或相似信道上进行训练与测试来验证其性能。然而，数据驱动算法在未接受训练的信道上往往表现显著退化，这是由于它们无法外推其训练知识所致。在线训练能够微调离线训练的神经网络以补偿这种退化，但其可行性受到所需巨大计算资源的挑战。为解决这一退化问题，我们提出了设计准则，旨在生成训练数据集，确保神经网络能够稳健地泛化至不同信道。此设计准则亦限制了训练神经网络能够泛化的信道范围。基于所提出的准则，我们进一步提出了基准设计，以实现最大泛化。通过这种方式，离线训练的神经网络仍能预测完整的信道矩阵，从而实现频率和时间插值，即使在它们未接受训练的信道上。为了展示其通用适用性，我们采用了不同复杂程度的神经网络来展示所实现的泛化能力。从模拟结果来看，训练神经网络实现了对具有固定信道特性和可变延迟散布的无线信道的稳健泛化。我们还研究了训练数据集的噪声效应以及系统的可扩展性。本文从新视角为迈向通用人工智能（AGI）迈出了关键一步。