TY - JOUR ID - 4719 TI - Generative Adversarial Networks for Propagation Channel Modeling JO - AUT Journal of Modeling and Simulation JA - MISCJ LA - en SN - 2588-2953 AU - SeyedSalehi, Shirin AU - Pourahamdi, Vahid AU - Sheikhzadeh, Hamid AU - Hossein Gharari, Ali AD - Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 53 IS - 2 SP - 217 EP - 234 KW - Generative adversarial networks KW - Image-to-image translation KW - Propagation Channel modeling KW - Statistical model evaluation DO - 10.22060/miscj.2022.20174.5250 N2 - Channel is one of the most important parts of a communication system as the medium of the propagation of electromagnetic waves. Being aware of how the channel affects the propagation waves is essential for the design, optimization, and performance analysis of a communication system. Along with conventional modeling schemes, in this paper, we present a novel propagation channel model. The proposed modeling strategy considers the 2-dimensional time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative Adversarial Networks (DCGANs). In addition, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN is used as an image-to-image translation technique to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using a few existing evaluation metrics. Furthermore, as modeling the 2D time- frequency response is more general than the modeling of the channel only in time, the conventional metrics for evaluation of the channel models are not sufficient; therefore, a new metric is introduced in this paper. This metric is based on the Cepstral Distance Measure (CDM) between the mean autocorrelation of the generated samples and measurement data. Using this metric, the generated channels show significant statistical similarity to the measurement data. UR - https://miscj.aut.ac.ir/article_4719.html L1 - https://miscj.aut.ac.ir/article_4719_2e8d8f11ed04c4ce11d1f3e6cd268120.pdf ER -