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This paper presents a model-driven generative adversarial network (GAN)-based framework for channel modeling in IRS-aided wireless communication systems, addressing the challenges of accurately modeling high-dimensional cascaded channels. The proposed IRS-GAN and its extension, IRS-GAN-M, autonomously learn the reflected channel distributions for single-user and multiuser scenarios, respectively, while significantly reducing the complexity and parameter requirements compared to traditional methods. The effe

Author

sathibabuarepalli

Language

EN