论文标题
通过生成对抗网络的毫米波和THZ无线通信的多频通道建模
Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks
论文作者
论文摘要
现代的蜂窝系统越来越依赖于多个不连续频段中的同时通信来实现宏观多样性和增加的带宽。多频通信在毫米波（MMWave）和Terahertz（THZ）频率中尤为重要，因为这些频段通常与较低的频率相结合以达到稳健性。对这些系统的评估需要统计模型，该模型可以捕获多个频率的通道路径的联合分布。本文提出了一种基于神经网络的一般方法，用于培训多频性双向统计通道模型。在提出的方法中，每个方法都被描述为一个多群集集，并且对生成对抗网络（GAN）进行了训练以生成随机的多群集轮廓，其中生成的群集数据包括簇的角度和延迟以及随机接收的功率，Angular，Angular，Angular，Angular，Angular，Angular，Angular，Angular，and angular，Angular，and and Angular，Angular，Angular，and and Angular，以及延迟在不同的频率上分布在不同的频率上。该模型可以轻松应用于多频链接或网络层仿真。该方法是为了对28和140 GHz的城市微细胞链路进行建模，该链接已通过广泛的射线追踪数据进行了训练。该方法使最小的统计假设和实验表明该模型可以捕获频率之间有趣的统计关系。
Modern cellular systems rely increasingly on simultaneous communication in multiple discontinuous bands for macro-diversity and increased bandwidth. Multi-frequency communication is particularly crucial in the millimeter wave (mmWave) and Terahertz (THz) frequencies, as these bands are often coupled with lower frequencies for robustness. Evaluation of these systems requires statistical models that can capture the joint distribution of the channel paths across multiple frequencies. This paper presents a general neural network based methodology for training multi-frequency double directional statistical channel models. In the proposed approach, each is described as a multi-clustered set, and a generative adversarial network (GAN) is trained to generate random multi-cluster profiles where the generated cluster data includes the angles and delay of the clusters along with the vectors of random received powers, angular, and delay spread at different frequencies. The model can be readily applied for multi-frequency link or network layer simulation. The methodology is demonstrated on modeling urban micro-cellular links at 28 and 140 GHz trained from extensive ray tracing data. The methodology makes minimal statistical assumptions and experiments show the model can capture interesting statistical relationships between frequencies.
