This paper focuses on millimeter wave (mmWave) channel prediction by machine learning (ML) methods. Previous ML-based mmWave channel predictors have limitations on requirements of the amount of training data, model generalization ability, robustness to noise, etc. In this paper, we propose a CNN model with a novel feature selection strategy for mmWave channel prediction. Automatic hyperparameter tuning (AHT) algorithms are embedded in the training process to iteratively optimize the predictive performance of the proposed CNN. The diversification strategy is leveraged to enhance the robustness of the AHT procedure against different communication scenarios. To improve the generalization ability of the prediction model, the input features are designed to capture the correlation between the physical environment and the channel characteristics. In parallel, the Cartesian coordinates of the transmitter (Tx) and receiver (Rx) are transformed into polar ones to reduce the model's sensitivity to coordinate noise. Numerical results demonstrate the effectiveness of the proposed CNN model in predicting mmWave channel characteristics in various communication scenarios.

中文翻译：

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用于毫米波信道预测的具有自动超参数调整的广义 CNN 模型

本文重点研究通过机器学习（ML）方法进行毫米波（mmWave）信道预测。先前基于机器学习的毫米波信道预测器在训练数据量、模型泛化能力、对噪声的鲁棒性等要求方面存在局限性。在本文中，我们提出了一种具有新颖特征选择策略的 CNN 模型，用于毫米波信道预测。自动超参数调整（AHT）算法嵌入到训练过程中，以迭代优化所提出的 CNN 的预测性能。利用多样化策略来增强 AHT 过程针对不同通信场景的鲁棒性。为了提高预测模型的泛化能力，输入特征被设计为捕获物理环境和信道特征之间的相关性。同时，发射器 (Tx) 和接收器 (Rx) 的笛卡尔坐标被转换为极坐标，以降低模型对坐标噪声的敏感性。数值结果证明了所提出的 CNN 模型在预测各种通信场景中毫米波信道特性方面的有效性。