AI 中文总结
该研究针对几何确定性无线信道建模的高复杂度问题，提出用信道参数图像训练生成式神经网络构建GBSM，该模型可捕获多径相关性、可靠插值且仿真结果与射线追踪数据高度匹配，是高效的信道建模替代方案。
AI 中文摘要
由于几何确定性无线信道建模复杂度高且实现困难，基于几何的随机信道建模（GBSM）方法已被用于评估无线通信的系统性能。本文提出一种新方法，通过使用信道参数形成的图像训练生成式神经网络来建模GBSM。为此，我们以图像形式处理信道参数数据，并训练生成式神经网络，其中主要采用卷积层来捕获多径分量之间的相关性。通过案例研究，我们证明使用信道图像有助于生成模型的训练，并确保模型学习到多径分量之间的相关性。我们表明，生成模型的输出忠实地表示了原始数据的联合分布，且训练后的模型能在训练期间未使用的保留条件下可靠插值，证明其作为直接重采样射线追踪数据库的高效数据替代方案具有实用价值。此外，为验证训练后模型的适用性，我们运行了简单的系统级仿真，结果显示训练后模型得到的结果与射线追踪数据的结果高度匹配。因此，所提出的模型有望减轻通用无线条件下GBSM实现的负担，并捕获原始信道数据的统计联合分布。
英文摘要
Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate system performance of wireless communications. This paper introduces a new method to model a GBSM by training a generative neural network using images formed by channel parameters. Toward this end, we process the data of channel parameters in the form of images and train the generative neural networks where the convolutional layers are mainly employed to capture correlation among multipath components. Through a case study, we demonstrate that the use of channel images facilitates the training of the generative model and ensures that the model learns the correlations among multipath components. We show that the outputs of the generative model faithfully represent the joint distributions of the original data, and that the trained model reliably interpolates across held-out conditions not used during training, demonstrating its practical value as a data-efficient alternative to directly resampling the ray-tracing database. Furthermore, to corroborate applicability of the trained model, we run simple system-level simulations and show the results obtained from the trained model closely match those from the ray-tracing data. Therefore, the proposed model is expected to ease the burden of GBSM implementations with general wireless conditions and capture the statistical joint distributions of the original channel data.
CommentsImplementation code is uploaded in the following GitHub repository https://github.com/sk8053/GeoStochasticChanModel
